Realising robust low speed sensorless PMSM control using current derivatives obtained from standard current sensors

David Hind, Chen Li, Mark Sumner, Chris Gerada

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

6 Citations (Scopus)

Abstract

This paper describes the implementation of a simple, robust and cost-effective sensorless control technique for PMSM machines. The method uses stator current derivative measurements made in response to certain PWM vectors. In this work the derivatives are created from measurements made with standard hall-effect sensors (at the start and end of switching vectors), meaning that specialist transducers, such as Rogowski Coils, are not required. However, under narrow PWM vectors high frequency (HF) oscillations can disrupt the current and current derivative responses. In previous work, the time that PWM vectors were applied to the machine for was extended to a threshold known as the minimum pulse width (tmin) in order to allow the HF oscillations to decay and a derivative measurement to be obtained. This introduces additional distortion to the motor current. It is shown here that an artificial neural network can be used to estimate derivatives using measurements from a standard current sensor before the HF oscillations have fully decayed, thus permitting a reduction of the minimum pulse width (and associated distortion).

Original languageEnglish
Title of host publication2017 IEEE International Electric Machines and Drives Conference, IEMDC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509042814
DOIs
Publication statusPublished - 3 Aug 2017
Externally publishedYes
Event2017 IEEE International Electric Machines and Drives Conference, IEMDC 2017 - Miami, United States
Duration: 21 May 201724 May 2017

Publication series

Name2017 IEEE International Electric Machines and Drives Conference, IEMDC 2017

Conference

Conference2017 IEEE International Electric Machines and Drives Conference, IEMDC 2017
Country/TerritoryUnited States
CityMiami
Period21/05/1724/05/17

Keywords

  • Current derivative
  • Neural Network
  • PMSM
  • Permanent Magnet
  • Saliency
  • Sensorless Control

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Mechanical Engineering

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