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

Indriawati, Katherin; Wijaya, Febry Pandu; Mufit, Choirul

doi:10.1109/icitee49829.2020.9271744

Cited by 1 publication

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The ripple component based estimator has drawbacks in terms of problems that arise due to Int J Pow Elec & Dri Syst ISSN: 2088-8694  noise [3]. There are several algorithms regarding the induction motor speed estimator such as Luenberger observer (LO), sliding mode observer (SMO), artificial neural network (ANN) based observer, model reference adaptive system (MRAS), and extended Kalman filter (EKF) observer are described in the literatures [4]- [19]. To overcome the problem of nonlinearity, an EKF is proposed so that the estimation error can be minimized throughout the speed range.…”

Section: Introductionmentioning

confidence: 99%

Implementation of disturbance observer for sensorless speed estimation in induction motor

Indriawati

Wijaya

Mufit

2022

IJPEDS

Self Cite

View full text Add to dashboard Cite

The use of speed sensors in induction motors is considered to be less effective because of the high price and diminishing reliability. Over time, a speed estimator was developed to increase the speed of the sensor. This paper describes the application of a disturbance observer algorithm as an estimator of the speed of an induction motor. In this case, the estimator is seen as an embedded system that only uses current information to measure speed of induction motor. To test the performance of the disturbance observer-based estimator, a comparison was made with the estimator based on the extended Kalman filter. From the experiment, it is found that the speed estimation using the disturbance observer has a smaller root mean square error in the low-speed operation than one of the extended Kalman filter, i.e. the root mean squared error (RMSE) value is below 4% in the range of 250 revolution per minute (RPM)–600 RPM.

show abstract

Section: Introductionmentioning

confidence: 99%