Novel Flux Linkage Estimation Algorithm for a Variable Flux PMSM

This paper presents a novel algorithm for online rotor flux linkage estimation for a variable flux interior permanent magnet synchronous machine drive system at different flux density levels. A modified adaptive nonlinear filter is used to instantaneously estimate the amplitude, phase angle, and fre...

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Vydáno v:IEEE transactions on industry applications Ročník 54; číslo 3; s. 2319 - 2335
Hlavní autoři: Aljehaimi, Akrem Mohamed, Pillay, Pragasen
Médium: Journal Article
Jazyk:angličtina
Vydáno: IEEE 01.05.2018
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ISSN:0093-9994, 1939-9367
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Shrnutí:This paper presents a novel algorithm for online rotor flux linkage estimation for a variable flux interior permanent magnet synchronous machine drive system at different flux density levels. A modified adaptive nonlinear filter is used to instantaneously estimate the amplitude, phase angle, and frequency of the major back EMF harmonic components, from which the total air-gap flux linkage is estimated. The algorithm avoids the averaging method that depends only on the fundamental back EMF component in estimating the air-gap flux linkage. The d -axis inductance versus current measurement test is performed at variable magnetization states to account for the d -axis inductance variation when estimating the rotor flux linkage. Since the stator winding resistance is temperature dependent, a thermocouple is used to obtain the actual stator winding temperature for accurate stator winding resistance measurement. The core loss resistance has been neglected for simplicity. The method was experimentally evaluated for different magnetization states and showed a good performance in tracking the rotor flux linkage variations. The method was tested for field-oriented control (FOC) schemes (<inline-formula> <tex-math notation="LaTeX">{\boldsymbol{i}_{\boldsymbol{d}}} = 0</tex-math></inline-formula>) and for FOC with the negative <inline-formula><tex-math notation="LaTeX">d</tex-math></inline-formula>-axis current operation as well.
ISSN:0093-9994
1939-9367
DOI:10.1109/TIA.2018.2794338