Highly Adaptive Voltage Disturbance Decomposition‐Based Online Diagnosis and Classification of Stator Electrical Faults in PMSMs

Le résumé fourni par la source

Stator electrical faults, including interturn faults (ITF) and high-resistance connection faults (HRC), are common faults in permanent magnet synchronous motors (PMSMs). Conventional model-based diagnostic methods typically rely on precise parameter modeling and are restricted to specific control strategies, which limits their ability to achieve online real-time fault diagnosis and accurate fault classification. To overcome these issues, this article first analyzes the characteristics of voltage disturbances in theαβ-frame under fault conditions while considering various control strategies (e.g.,id= 0, MTPA, and flux weakening). Then, a highly adaptive voltage disturbance decomposition scheme is proposed by leveraging the disturbance observation capability of an extended state observer (ESO). The proposed scheme can separate the observed voltage disturbances into resistive and inductive differential-mode components effectively, that reflects the different physical behaviors between healthy and fault conditions clearly. Thereby, it can reduce dependency on precise modeling and overcome classification challenges. Finally, experimental results have verified that the proposed method exhibits high robustness and low computational complexity in online fault diagnosis and classification across diverse operating conditions, underscoring its potential for practical applications.

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