Inter-turn stator winding fault diagnosis and determination of fault percent in PMSM

Nejad, Mohammad Ali Shamsi; Taghipour, Mohammad

doi:10.1109/iapec.2011.5779847

Cited by 21 publications

(1 citation statement)

References 3 publications

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“…The root mean square value of the fault phase current increases with higher fault levels [66,67]. Various methods, such as MCSA and EPVA, have been proposed for ITSF fault detection, focusing on fault components, such as the second harmonic component of the q-axis current, and utilizing the park transform of the stator current [68].…”

Section: Diagnostic Methods Based On Stator Current Signalmentioning

confidence: 99%

An Overview of Diagnosis Methods of Stator Winding Inter-Turn Short Faults in Permanent-Magnet Synchronous Motors for Electric Vehicles

Jiang,

Ji,

Zhang

et al. 2024

WEVJ

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This article provides a comprehensive overview of state-of-the-art techniques for detecting and diagnosing stator winding inter-turn short faults (ITSFs) in permanent-magnet synchronous motors (PMSMs) for electric vehicles (EVs). The review focuses on the following three main categories of diagnostic approaches: motor model-based, signal processing-based, and artificial intelligence (AI)-based fault detection and diagnosis methods. Motor model-based methods utilize motor state estimation and motor parameter estimation as the primary strategies for ITSF diagnosis. Signal processing-based techniques extract fault signatures from motor measured data across time, frequency, or time-frequency domains. In contrast, AI-based methods automatically extract higher-order fault signatures from large volumes of preprocessed data, thereby enhancing the effectiveness of fault diagnosis. The strengths and limitations of each approach are thoroughly examined, providing valuable insights into the advancements in ITSF detection and diagnosis techniques for PMSMs in EV applications. The emphasis is placed on the application of signal processing methods and deep learning techniques in the diagnosis of ITSF in PMSMs in EV applications.

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Section: Diagnostic Methods Based On Stator Current Signalmentioning

confidence: 99%