Improved speed and load torque estimations with adaptive fading extended Kalman filter

Zerdali E., Yildiz R., Inan R., Demir R., Barut M.

INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS, vol.31, no.1, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 31 Issue: 1
  • Publication Date: 2021
  • Doi Number: 10.1002/2050-7038.12684
  • Journal Name: INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Keywords: adaptive fading extended Kalman filter, induction motor, parameter estimation, speed&#8208, sensorless control, state estimation, SENSORLESS CONTROL, STABILITY, DRIVES
  • Kayseri University Affiliated: No

Abstract

Background Extended Kalman filter (EKF) is one of the most preferred observers for state and parameter estimation of induction motor. To achieve optimal estimations, EKFs require a stochastic system with complete dynamic or measurement equation. However, those equations are partially known in practice and may vary depending on operating conditions, leading to a degradation in the estimation performance of conventional EKFs (CEKFs).