1990
DOI: 10.1109/20.104905
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An improved approach to power losses in magnetic laminations under nonsinusoidal induction waveform

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Cited by 311 publications
(150 citation statements)
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“…The presented methodology of iron loss computation has its roots in the loss separation theory by Bertotti [15] and the Steinmetz theory generalized by Fiorillo [16]. Our proposed model brinks a new component in these theories, namely the segregation of hysteresis loss into rotational and alternating ones, which behave in very different ways.…”
Section: Discussionmentioning
confidence: 99%
“…The presented methodology of iron loss computation has its roots in the loss separation theory by Bertotti [15] and the Steinmetz theory generalized by Fiorillo [16]. Our proposed model brinks a new component in these theories, namely the segregation of hysteresis loss into rotational and alternating ones, which behave in very different ways.…”
Section: Discussionmentioning
confidence: 99%
“…The most extended way to define the iron losses is by the widely known loss separation method in which they are divided in three main components: hysteresis losses, classical Eddy losses and excess losses [6,7]. In case of non sinusoidal voltages, the high order harmonics of currents can accentuate the presence of some phenomena such as the skin effect or the minor loops which are not directly taken into account by theses classical methods.…”
Section: Case Study 1: Iron Losses Computation In Electrical Machinesmentioning
confidence: 99%
“…The prediction iron losses of the existing model keep constant while the actual iron losses vary significantly when temperature changes. On the other hand, the improved model (6) can predict the iron losses with low and stable relative prediction errors even when the temperature changes significantly. This is due to the fact that the improved model (6) can track the iron loss variation with temperature.…”
Section: B Modelling Of Temperature Dependent Coefficientsmentioning
confidence: 99%
“…On the other hand, the improved model (6) can predict the iron losses with low and stable relative prediction errors even when the temperature changes significantly. This is due to the fact that the improved model (6) can track the iron loss variation with temperature. This means that the improved model can consider the temperature influence on iron losses effectively.…”
Section: B Modelling Of Temperature Dependent Coefficientsmentioning
confidence: 99%
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