JOURNAL ARTICLE

Induction Motors Fault Detection Using Square-Root Transformed Cubature Quadrature Kalman Filter

Jafar ZareiElham KowsariRoozbeh Razavi‐Far

Year: 2018 Journal:   IEEE Transactions on Energy Conversion Vol: 34 (2)Pages: 870-877   Publisher: Institute of Electrical and Electronics Engineers

Abstract

This paper proposes a novel approach for simultaneous state estimation and broken rotor bars (BRBs) detection in induction motors. The proposed method is named square-root transformed cubature quadrature Kalman filter (SR-TCQKF). The accuracy of the filter has been improved by a transformation on the filter's sigma points, whereas the filter becomes more stable due to the use of the SR version of the covariance matrix. Accordingly, the SR-TCQKF is more accurate and stable than the standard cubature quadrature Kalman filter. The efficiency of the SR-TCQKF has been evaluated for speed estimation and BRB detection in induction motors. The attained results show the efficiency of the proposed approach.

Keywords:
Control theory (sociology) Kalman filter Extended Kalman filter Induction motor Ensemble Kalman filter Alpha beta filter Square root Quadrature (astronomy) Invariant extended Kalman filter Filter (signal processing) Fault detection and isolation Fast Kalman filter Mathematics Covariance matrix Sigma Computer science Engineering Algorithm Physics Electronic engineering Statistics Moving horizon estimation Artificial intelligence Geometry

Metrics

33
Cited By
2.48
FWCI (Field Weighted Citation Impact)
32
Refs
0.90
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Machine Fault Diagnosis Techniques
Physical Sciences →  Engineering →  Control and Systems Engineering
Fault Detection and Control Systems
Physical Sciences →  Engineering →  Control and Systems Engineering
Target Tracking and Data Fusion in Sensor Networks
Physical Sciences →  Computer Science →  Artificial Intelligence

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