Three-Level NPC Inverter Incipient Fault Detection and Classification using Output Current Statistical Analysis

Abstract : This paper deals with open switch Fault Detection and Diagnosis (FDD) in three-level Neutral Point Clamped (NPC) inverter for electrical drives. The approach is based on the already available phase current time series measurements for different operating conditions (motor speed, load, and environment noise). Both fault detection and classification are studied and the efficiency performances of the proposed selected features are shown. For the fault detection, we focus on the first four statistical moments and the extracted features and then the Cumulative Sum (CUSUM) algorithm as the feature analysis technique to improve the performances. For the classification study, we propose to couple the knowledge on the faulty system brought by the statistical moments and the Kullback-Leibler divergence particularly suitable for the detection of incipient changes. The Principal Component Analysis (PCA) is then used to perform the classification. A 2D framework is obtained, which allows the faults to be classified efficiently within the considered operating conditions for all the selected fault durations.
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Mehdi Baghli, Claude Delpha, Demba Diallo, Abdelhamid Hallouche, David Mba, et al.. Three-Level NPC Inverter Incipient Fault Detection and Classification using Output Current Statistical Analysis. Energies, MDPI, 2019, 12 (7), pp.1372. ⟨10.3390/en12071372⟩. ⟨hal-02147094⟩

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