Fast Evaluation of Self-Sensing Capability of Synchronous Machines

Convergence region and estimation error of high frequency injection sensorless control strictly depend on the selected synchronous machine and on the actual operating point. An accurate knowledge of the motor parameters is required to compute the self-sensing capability range. The aim of this article is to show a fast solution to identify the convergence region based on incremental parameters approximation with less experimental measurements than the conventional approach. The proposed method to reconstruct the motor convergence region is fully offline. It requires few samples of the inverse incremental inductances, which are processed through some math and a simple fitting procedure. The method is validated through experimental measurements for two different types of synchronous motors for a sensorless drive application.

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Publication Details

Journal
IEEE Transactions on Industrial Electronics
Published
2024-12-03
DOI
https://doi.org/10.1109/tie.2024.3497315
Primary Topic
Neural Networks and Applications
Type
article
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article

Fast Evaluation of Self-Sensing Capability of Synchronous Machines

Alice Maimeri, Luigi Alberti, Matteo Berto
IEEE Transactions on Industrial Electronics
Neural Networks and Applications
article

Fast Evaluation of Self-Sensing Capability of Synchronous Machines

Alice Maimeri, Luigi Alberti, Matteo Berto
article en

Abstract

Convergence region and estimation error of high frequency injection sensorless control strictly depend on the selected synchronous machine and on the actual operating point. An accurate knowledge of the motor parameters is required to compute the self-sensing capability range. The aim of this article is to show a fast solution to identify the convergence region based on incremental parameters approximation with less experimental measurements than the conventional approach. The proposed method to reconstruct the motor convergence region is fully offline. It requires few samples of the inverse incremental inductances, which are processed through some math and a simple fitting procedure. The method is validated through experimental measurements for two different types of synchronous motors for a sensorless drive application.

IEEE Transactions on Industrial ElectronicsVol. 72(7)
University of Padua (IT)
Affordable and clean energy
Openalex Percentile: Top 80%
Neural Networks and Applications
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