An Automatic Diagnosis of an Inverter IGBT Open-Circuit Fault Based on HHT-ANN

Abstract

The main objective of this paper is to propose a method that contributes to the automatic diagnosis of the IGBT open-circuit fault of an inverter for detecting and localizing the fault using the stator current spectral analysis technique. The proposal focusses on the use of the combination of signal processing and artificial intelligence techniques for the detection and localization of the fault. The proposed diagnosis method begins first by using the Hilbert-Huang transform (HHT) to detect the harmonic characterizing the fault based on the complete empirical ensemble mode decomposition (CEEMD) of the three-stator currents (i_as, i_bs, i_cs). The CEEMD provides the intrinsic mode function (IMF) which contains information of the IGBT open-circuit fault. For the exact choice of the IMF, a statistical study based on the calculation of the root mean square values (RMS) is carried out for each IMF. The IMF choice depends on the condition that the RMS values of the inverter upper IGBTs are always lower than the RMS values of the complementary ones. The results obtained can be seen to respond well to the RMS condition and the spectral envelope of the IMF₁ makes it possible to detect the harmonic characterizing the inverter IGBT open-circuit fault. The proposed diagnosis method then moves to the use of the artificial neural network (ANN) to localize the faulty IGBT. The results obtained using the proposed method are validated experimentally and demonstrate well their effectiveness with a very high classification rate.

Keywords:

• inverter
• IGBT
• open-circuit fault
• HHT
• EMD
• CEEMD
• IMF
• spectral envelope
• RMS
• ANN

Additional information

Notes on contributors

Bilal Djamal Eddine Cherif

Bilal Djamal Eddine Cherif received his Engineering degree from the University of M’sila, Algeria in 2010, his Magister Degree and his Ph.D degree in Electrical Engineering from the University of Sciences and Technology of Oran (USTO-MB), Algeria in 2015 and 2019. He is currently Professor Lecturer and researcher in Electrical Engineering department, Faculty of Technology at University of M’sila Algeria. His research interests include: electrical machines and drives modeling and analysis, electrical machines and drives control and converters as well as electrical machines and drives faults diagnosis and tolerance.

Azeddine Bendiabdellah

Azeddine Bendiabdellah received his Bachelor Engineering degree with honors and his Ph.D degree from the University of Sheffield, England, in 1980, and 1985 respectively. From 1990 to 1991 he was a visiting professor at Tokyo Institute of Technology (T.I.T), Japan. He is currently Professor lecturer and researcher in Electrical Engineering Faculty at the University of Sciences and Technology of Oran, (USTO-MB) Algeria. His research interests include: electrical machines and drives modeling and analysis, electrical machines and drives control and converters as well as electrical machines and drives faults diagnosis and tolerance.

Mostefa Tabbakh

Mostefa Tabbakh received his Engineer degree in Electronics and Magister degree in industrial control from M’sila University in 2002 and 2007 respectively. He was recruited in 2014 as electronics assistant professor in University of M'sila. Member of research projects at University of M'sila and Electrical Engineering Laboratory of M'sila University. His research interests include: electrical machines and drives faults diagnosis and tolerance, power quality conditioning, DSP and digital control, control and diagnostic.