Article
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Sensorless Capability Expansion for SPMSM based on Inductance Parameter Identification
Version 1
: Received: 24 May 2024 / Approved: 27 May 2024 / Online: 27 May 2024 (05:41:20 CEST)
How to cite: Chen, P.; Ma, R.; Chen, Z. Sensorless Capability Expansion for SPMSM based on Inductance Parameter Identification. Preprints 2024, 2024051675. https://doi.org/10.20944/preprints202405.1675.v1 Chen, P.; Ma, R.; Chen, Z. Sensorless Capability Expansion for SPMSM based on Inductance Parameter Identification. Preprints 2024, 2024051675. https://doi.org/10.20944/preprints202405.1675.v1
Abstract
Pulsating high frequency voltage injection can be used for sensorless control of SPMSM at zero and low speed ranges. However, the sensorless capability still faces challenges to the requirements of industrial application, especially at heavy load status. Aiming at this issue, this article proposes a sensorless capability expansion method for SPMSM based on inductance parameter identification. First, incremental inductances at d-q-axis and cross-coupling inductance are identified by three steps combining the rotating high frequency voltage injection and pulsating high frequency voltage injection, then, using polynomial curve fitting algorithm, apparent inductances are calculated. Second, positive DC current injection at d-axis is proposed to enhance the saliency ratio based on the analysis of parameter identification results. Compared with the conventional =0 or < 0 method, when positive DC current is injected at d-axis, saliency ratio is enhanced. Third, convergence region is expanded at heavy load status and accuracy of rotor position estimation is improved using the proposed method. Finally, the experiment results validate that the sensorless capability of SPMSM is expanded.
Keywords
sensorless capability; inductance parameter identification; saliency ratio; convergence region; heavy load status; SPMSM
Subject
Engineering, Electrical and Electronic Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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