A robust stator inter-turn fault detection in induction motor utilizing Kalman filter-based algorithm. (January 2022)

• Record Type: Journal Article
• Authors: Namdar, Ali; Samet, Haidar; Allahbakhshi, Mehdi; Tajdinian, Mohsen; Ghanbari, Teymoor
• Is Part Of: Measurement. Volume 187(2022)
• Publication Date: 2022-01
• Subjects: Induction motors (IMs) -- Stator inter-turn fault (SITF) -- Voltage quality problem -- Kalman filter (KF); Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2021.110181
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: Recursive and straightforward formulation based on KF is presented. An index based on the standard deviation of the residual signal is introduced. Also an auxiliary index based on the voltage signal is utilized. The performance of the proposed indices is investigated with experimental data. Abstract: Today, due to the widespread utilization of Induction Motors (IMs) in different industries, their condition monitoring is of great importance. Concentrating on the failures of IMs, it has been acknowledged that the stator inter-turn faults (SITFs) are the most frequent electrical failures. This paper puts forward an algorithm for SITF detection founded on Kalman Filter (KF). More specifically, the proposed algorithm employs KF to extract motor current signatures (MCS) and motor voltage signatures (MVS). Afterward, a statistical SITF index is used, based on the standard deviation of the extracted signatures. The proposed SITF index is technically robust against non-fault conditions including voltage quality problems and heavy load changes as well as has a significant performance in the presence of high measured noise-impregnated signals due to utilization of KF algorithm-based. Moreover, during source harmonic pollutions, the proposed algorithm has a very robust performance. Also, due to straightforward and low-computational mathematical basis, the proposed method is computationally efficient. The performance of the proposed method is validated with numerous simulation and experimental scenarios. The results indicate the proposed SITF index has robust performance, promising accuracy and good speed of convergence.