Doubly Fed Induction Machine stator fault diagnosis under time-varying conditions based on frequency sliding and wavelet analysis

Abstract

The paper introduces a monitoring and diagnostic technique for the detection of incipient stator electrical faults in Doubly Fed Induction Machine (DFIM) for wind power systems. Operating in aggressive environments, the detection of anomalies at an incipient stage is crucial to decide about the operating continuity of the machines. Discrete Wavelet Transform (DWT) is used to detect stator faults under time varying-condition in two mainly different contexts: Transient-Speed conditions and Fault-Varying conditions. A frequency sliding (FS) with High Multiresolution Analysis (HMRA) approach is proposed for improving the ability of DWT in extracting the most relevant stator fault frequency component dynamically over time thereby. A dynamic mean power calculation at different resolution levels was introduced as a diagnostic index to quantify the fault extent. Simulation and experimental results show the effectiveness of the proposed approach in discriminating stator fault severities leading to an effective diagnostic procedure for stator faults in DFIM.