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Energies 2019, 12(7), 1372; https://doi.org/10.3390/en12071372

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

1
Laboratoire IRECOM, University Djillali Liabès, 22000 Sidi Bel Abbes, Algeria
2
GeePs, UMR 8507, CNRS, CentraleSupélec, Univ. Paris Sud, Université Paris Saclay, Sorbonne Univ, 75006 Paris, France
3
L2S, UMR 8506, CNRS, CentraleSupélec, Univ. Paris Sud, Université Paris Saclay, 91190 Saint-Aubin, France
4
Shanghai Maritime University, Department of Electrical Automation, Shanghai 201306, China
5
Faculty of Computer Engineering and Media, De Montfort University, Leicester LE1 9BH, UK
*
Author to whom correspondence should be addressed.
Received: 20 March 2019 / Revised: 2 April 2019 / Accepted: 2 April 2019 / Published: 9 April 2019
(This article belongs to the Special Issue Fault Diagnosis and Fault-Tolerant Control)
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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. View Full-Text
Keywords: three-level neutral point clamped (NPC) inverter; open switch fault (OSF); intermittent fault monitoring; incipient fault detection and diagnosis (FDD); cumulated sum (CUSUM); kullback-Leibler divergence (KLD); principal component analysis (PCA) three-level neutral point clamped (NPC) inverter; open switch fault (OSF); intermittent fault monitoring; incipient fault detection and diagnosis (FDD); cumulated sum (CUSUM); kullback-Leibler divergence (KLD); principal component analysis (PCA)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Baghli, M.; Delpha, C.; Diallo, D.; Hallouche, A.; Mba, D.; Wang, T. Three-Level NPC Inverter Incipient Fault Detection and Classification using Output Current Statistical Analysis. Energies 2019, 12, 1372.

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