3 Citations (Scopus)

Abstract

This paper addresses the problem of nonlinear time-varying state and parameter estimation of induction machines (IMs) on the basis of a third-order electrical model. The objectives of this paper are threefold. The first objective is to propose the use of an improved particle filter (IPF) with better proposal distribution for nonlinear and non-Gaussian state and parameter estimation. The second objective is to extend the state and parameter estimation techniques (i.e., extended Kalman filter (EKF), unscented Kalman filter (UKF), particle filter (PF), and IPF) to better handle nonlinear and non-Gaussian processes without a priori state information, by utilizing a time-varying assumption of statistical parameters. In this case, the state vector to be estimated at any instant is assumed to follow a Gaussian model, where the expectation and the covariance matrix are both random. The third objective is to compare the performances of EKF, UKF, PF, and IPF in estimating the states of the power process model representing the IM (i.e, the rotor speed, the rotor flux, the stator flux, the rotor resistance, and the magnetizing inductance) and their abilities to estimate some of the key system parameters, which are needed to define the IM process model. The results show that the IPF provides a significant improvement over the PF because, unlike the PF, which depends on the choice of sampling distribution used to estimate the posterior distribution, the IPF yields an optimum choice of the sampling distribution, which also accounts for the observed data. This conclusion is also supported by the experimental results.

Original languageEnglish
Pages (from-to)905-924
Number of pages20
JournalInternational Journal of Adaptive Control and Signal Processing
Volume29
Issue number7
DOIs
Publication statusPublished - 1 Jul 2015

Fingerprint

State estimation
Parameter estimation
Rotors
Extended Kalman filters
Kalman filters
Fluxes
Sampling
Covariance matrix
Inductance
Stators

Keywords

  • particle filtering
  • power systems
  • state/parameter estimation
  • time varying

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

@article{f5484b8265484eb095909df4a4575542,
title = "Bayesian methods for time-varying state and parameter estimation in induction machines",
abstract = "This paper addresses the problem of nonlinear time-varying state and parameter estimation of induction machines (IMs) on the basis of a third-order electrical model. The objectives of this paper are threefold. The first objective is to propose the use of an improved particle filter (IPF) with better proposal distribution for nonlinear and non-Gaussian state and parameter estimation. The second objective is to extend the state and parameter estimation techniques (i.e., extended Kalman filter (EKF), unscented Kalman filter (UKF), particle filter (PF), and IPF) to better handle nonlinear and non-Gaussian processes without a priori state information, by utilizing a time-varying assumption of statistical parameters. In this case, the state vector to be estimated at any instant is assumed to follow a Gaussian model, where the expectation and the covariance matrix are both random. The third objective is to compare the performances of EKF, UKF, PF, and IPF in estimating the states of the power process model representing the IM (i.e, the rotor speed, the rotor flux, the stator flux, the rotor resistance, and the magnetizing inductance) and their abilities to estimate some of the key system parameters, which are needed to define the IM process model. The results show that the IPF provides a significant improvement over the PF because, unlike the PF, which depends on the choice of sampling distribution used to estimate the posterior distribution, the IPF yields an optimum choice of the sampling distribution, which also accounts for the observed data. This conclusion is also supported by the experimental results.",
keywords = "particle filtering, power systems, state/parameter estimation, time varying",
author = "Majdi Mansouri and Mohamed-Seghir, {Moustafa M.} and Hazem Nounou and Mohamed Nounou and Haitham Abu-Rub",
year = "2015",
month = "7",
day = "1",
doi = "10.1002/acs.2511",
language = "English",
volume = "29",
pages = "905--924",
journal = "International Journal of Adaptive Control and Signal Processing",
issn = "0890-6327",
publisher = "John Wiley and Sons Ltd",
number = "7",

}

TY - JOUR

T1 - Bayesian methods for time-varying state and parameter estimation in induction machines

AU - Mansouri, Majdi

AU - Mohamed-Seghir, Moustafa M.

AU - Nounou, Hazem

AU - Nounou, Mohamed

AU - Abu-Rub, Haitham

PY - 2015/7/1

Y1 - 2015/7/1

N2 - This paper addresses the problem of nonlinear time-varying state and parameter estimation of induction machines (IMs) on the basis of a third-order electrical model. The objectives of this paper are threefold. The first objective is to propose the use of an improved particle filter (IPF) with better proposal distribution for nonlinear and non-Gaussian state and parameter estimation. The second objective is to extend the state and parameter estimation techniques (i.e., extended Kalman filter (EKF), unscented Kalman filter (UKF), particle filter (PF), and IPF) to better handle nonlinear and non-Gaussian processes without a priori state information, by utilizing a time-varying assumption of statistical parameters. In this case, the state vector to be estimated at any instant is assumed to follow a Gaussian model, where the expectation and the covariance matrix are both random. The third objective is to compare the performances of EKF, UKF, PF, and IPF in estimating the states of the power process model representing the IM (i.e, the rotor speed, the rotor flux, the stator flux, the rotor resistance, and the magnetizing inductance) and their abilities to estimate some of the key system parameters, which are needed to define the IM process model. The results show that the IPF provides a significant improvement over the PF because, unlike the PF, which depends on the choice of sampling distribution used to estimate the posterior distribution, the IPF yields an optimum choice of the sampling distribution, which also accounts for the observed data. This conclusion is also supported by the experimental results.

AB - This paper addresses the problem of nonlinear time-varying state and parameter estimation of induction machines (IMs) on the basis of a third-order electrical model. The objectives of this paper are threefold. The first objective is to propose the use of an improved particle filter (IPF) with better proposal distribution for nonlinear and non-Gaussian state and parameter estimation. The second objective is to extend the state and parameter estimation techniques (i.e., extended Kalman filter (EKF), unscented Kalman filter (UKF), particle filter (PF), and IPF) to better handle nonlinear and non-Gaussian processes without a priori state information, by utilizing a time-varying assumption of statistical parameters. In this case, the state vector to be estimated at any instant is assumed to follow a Gaussian model, where the expectation and the covariance matrix are both random. The third objective is to compare the performances of EKF, UKF, PF, and IPF in estimating the states of the power process model representing the IM (i.e, the rotor speed, the rotor flux, the stator flux, the rotor resistance, and the magnetizing inductance) and their abilities to estimate some of the key system parameters, which are needed to define the IM process model. The results show that the IPF provides a significant improvement over the PF because, unlike the PF, which depends on the choice of sampling distribution used to estimate the posterior distribution, the IPF yields an optimum choice of the sampling distribution, which also accounts for the observed data. This conclusion is also supported by the experimental results.

KW - particle filtering

KW - power systems

KW - state/parameter estimation

KW - time varying

UR - http://www.scopus.com/inward/record.url?scp=84947493507&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84947493507&partnerID=8YFLogxK

U2 - 10.1002/acs.2511

DO - 10.1002/acs.2511

M3 - Article

AN - SCOPUS:84947493507

VL - 29

SP - 905

EP - 924

JO - International Journal of Adaptive Control and Signal Processing

JF - International Journal of Adaptive Control and Signal Processing

SN - 0890-6327

IS - 7

ER -