Online Parameter Estimation for Permanent Magnet Synchronous Machines: An Overview

Онлайн-оценивание параметров синхронных машин с постоянными магнитами: обзор
Z. Q. Zhu, Dawei Liang, Kan Liu
2021-01-01

inverter nonlinearitiesonline parameter estimationpermanent magnet synchronous machinesrank-deficient issuesensorless control
Online parameter estimation of permanent magnet synchronous machines is critical for improving their control performance and operational reliability. This paper provides an overview of the recent achievements of online parameter estimation of PMSMs with examples. The critical issues in parameter estimation are firstly analysed, especially the rank-deficient issue and inverter nonlinearities. Then, the state-of-the-art online parameter estimation modelling techniques are reviewed and assessed. Finally, some typical applications and examples are outlined, e.g. estimation of mechanical parameters, improvement of sensored and sensorless control performance, thermal condition monitoring, and fault diagnosis, together with future research trends.
1
Online parameter estimation is identified as critical for improving permanent magnet synchronous machine control performance and operational reliability.
2
Online parameter estimation supports mechanical parameter estimation, improved sensored and sensorless control, thermal condition monitoring, and fault diagnosis.
3
Recent state-of-the-art online parameter estimation modeling techniques are systematically reviewed and assessed.
4
The paper summarizes future research trends for online parameter estimation of PMSMs.
5
The review highlights rank deficiency and inverter nonlinearities as major challenges affecting online PMSM parameter estimation.

permanent magnet synchronous machines (PMSMs)

online parameter estimation, including its rank-deficient and inverter-nonlinearity challenges and applications to control performance improvement, thermal condition monitoring, and fault diagnosis

Publication Details
Publication Date
2021-01-01
Journal
Publisher
ISSN
Cited by
195
Access Type
Author Information
Authors
Z. Q. Zhu
Dawei Liang
Kan Liu
Explore further
Open the scid.ai AI chat with a ready-made request: it will find papers on a similar topic and help build a literature review.
Find similar papers in the chat
Make a presentation
100%