3 Citations (Scopus)

Abstract

Induction machine is highly nonlinear model with states that change with operating point and temperature. In these cases, estimating these variables from other easily obtained measurements can be extremely useful. This paper deals with the problem of state estimation of induction machine on the basis of a third-order electrical model using Bayesian methods. The performances of Bayesian estimation techniques are compared when they are utilized to achieve this objective. These techniques include the extended Kalman filter (EKF), the unscented Kalman filter (UKF), the particle filter (PF), and the developed improved particle filter (IPF). The estimation results, which are validated using simulations, show that IPF provides improved estimation performance over PF, even with abrupt changes in estimated states, and both of them can provide improved accuracy over UKF and EKF. These advantages of the IPF are due to the fact that it uses a better proposal distribution that takes the latest observation into account.

Original languageEnglish
DOIs
Publication statusPublished - 1 Jan 2014
Event2014 IEEE 11th International Multi-Conference on Systems, Signals and Devices, SSD 2014 - Castelldefels-Barcelona, Spain
Duration: 11 Feb 201414 Feb 2014

Other

Other2014 IEEE 11th International Multi-Conference on Systems, Signals and Devices, SSD 2014
CountrySpain
CityCastelldefels-Barcelona
Period11/2/1414/2/14

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Keywords

  • induction machine
  • particle filtering
  • power system
  • State estimation

ASJC Scopus subject areas

  • Signal Processing
  • Control and Systems Engineering

Cite this

Mansouri, M., Nounou, H., & Nounou, M. (2014). State estimation and application to induction machines - A comparative study. Paper presented at 2014 IEEE 11th International Multi-Conference on Systems, Signals and Devices, SSD 2014, Castelldefels-Barcelona, Spain. https://doi.org/10.1109/SSD.2014.6808792