Speed Estimation of a Double‐Star Permanent Magnet Synchronous Motor Using an Optimized Extended Kalman Filter

A mathematical model of a double‐star permanent magnet synchronous motor is proposed, coupled with an estimator for speed, position, and currents based on an extended Kalman filter. This filter is optimized using a novel methodology. The primary objective is to determine the optimal values for the t...

Full description

Saved in:
Bibliographic Details
Published in:International transactions on electrical energy systems Vol. 2025; no. 1
Main Authors: Naas, Badreddine, Nezli, Lazhari, Elbar, Mohamed, Zaitsev, Ievgen
Format: Journal Article
Language:English
Published: Hoboken John Wiley & Sons, Inc 01.01.2025
Wiley
Subjects:
Adaptive algorithms
Algorithms
Computer simulation
Covariance matrix
Embedded systems
Error analysis
Evolutionary algorithms
Evolutionary computation
Extended Kalman filter
Genetic algorithms
Kalman filters
Noise measurement
Parameter estimation
Particle swarm optimization
Permanent magnets
Population number
Process controls
ISSN:2050-7038, 2050-7038
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:A mathematical model of a double‐star permanent magnet synchronous motor is proposed, coupled with an estimator for speed, position, and currents based on an extended Kalman filter. This filter is optimized using a novel methodology. The primary objective is to determine the optimal values for the three noise covariance matrices to ensure the convergence of the parameter estimation. To enhance the estimator’s performance, several innovative optimization strategies are introduced. These combine different techniques, notably particle swarm optimization with the Nelder–Mead simplex algorithm, as well as a genetic algorithm coupled with this same hybrid method. Other approaches are also deployed, such as a fuzzy self‐tuning method for success‐history–based parameter adaptation for differential evolution, along with a fuzzy version of the linear population size reduction success‐history–based adaptive differential evolution algorithm. Furthermore, an accelerated procedure for initializing the covariance matrices is implemented. Specifically, the error estimation covariance matrix and the measurement noise covariance matrix are fixed, while the process noise covariance matrix is the subject of the optimization. The validity of the proposed approach is demonstrated through comprehensive numerical simulations. These simulations include the machine model, its power supply, and the parameter estimator, all implemented within the MATLAB/Simulink environment.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2050-7038
2050-7038
DOI:10.1155/etep/8466428