Comparing Particle Filter, Adaptive Extended Kalman Filter and Disturbance Observer for Induction Motor Speed Estimation

Katherin Indriawati, Febry Pandu Wijaya, Choirul Mufit

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

1 Citation (Scopus)

Abstract

Electric motors in industry are required to operate at a certain speed with varying loads. In general, speed and position information can be measured using an encoder or tachogenerator on a motor shaft, but it will affect the cost and complexity factors. To reduce the cost factor and increase the reliability and robustness of the system, this information can be estimated, known as speed sensorless. This paper discusses three model-based estimation algorithms: Disturbance Observer (DO), Particle Filter (PF), and Adaptive Extended Kalman Filter (AEKF). The main topic in this paper is to evaluate these algorithms in estimating induction motor speed. Based on the performance testing results of the three algorithms, namely using root mean square error (RMSE) value, it was found that the DO algorithm is better than compared to the AEKF and PF algorithms.

Original languageEnglish
Title of host publicationICITEE 2020 - Proceedings of the 12th International Conference on Information Technology and Electrical Engineering
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages57-62
Number of pages6
ISBN (Electronic)9781728110974
DOIs
Publication statusPublished - 6 Oct 2020
Event12th International Conference on Information Technology and Electrical Engineering, ICITEE 2020 - Virtual, Yogyakarta, Indonesia
Duration: 6 Oct 20208 Oct 2020

Publication series

NameICITEE 2020 - Proceedings of the 12th International Conference on Information Technology and Electrical Engineering

Conference

Conference12th International Conference on Information Technology and Electrical Engineering, ICITEE 2020
Country/TerritoryIndonesia
CityVirtual, Yogyakarta
Period6/10/208/10/20

Keywords

  • Adaptive Extended Kalman Filter
  • Disturbance Observer
  • Induction Motor
  • Particle Filter

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