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Convergence Analysis for Enhanced Reliability and Accuracy of Short-Circuit Fault Detection in Three-Phase Induction Motors Using data integration with Extended Kalman Filter
  • Samaneh Alsadat Saeedinia1, ,
  • Mahsa Mohaghegh ,
  • Saeed Ebadollahi
Samaneh Alsadat Saeedinia1,
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Mahsa Mohaghegh
Auckland University of technology

Corresponding Author:[email protected]

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Saeed Ebadollahi
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Abstract

The reliability and accuracy of detecting faults in induction motors are crucial for reducing maintenance costs, increasing safety, and improving efficiency in industrial applications. This paper proposes a method to enhance the reliability and accuracy of detecting short-circuit faults (SCF) in three-phase induction motors (IMs) by fusing data with the Extended Kalman Filter (EKF). The method addresses the challenge of selecting appropriate variances of measurement noise and process noise affecting EKF convergence by proposing a new algorithm for updating the process noise covariance based on EKF convergence conditions in decentralized data integration (DI). The proposed algorithm ensures convergence and improves fault detection accuracy, as demonstrated by comparing it to using only one estimator. This method shows promise in improving the reliability and accuracy of fault detection in induction motors, leading to significant benefits in industrial applications