Fault Investigation in Cascaded H-Bridge Multilevel Inverter through Fast Fourier Transform and Artificial Neural Network Approach

G. Kiran Kumar, E. Parimalasundar, D. Elangovan, P. Sanjeevikumar, Francesco Lannuzzo and Jens Bo Holm-Nielsen
Additional contact information
G. Kiran Kumar: School of Electrical Engineering, VIT Vellore, Tamil Nadu 632014, India
E. Parimalasundar: Department of Electrical and Electronics Engineering, Sree Vidyanikethan Engineering College, Tirupati 517102, India
D. Elangovan: School of Electrical Engineering, VIT Vellore, Tamil Nadu 632014, India
P. Sanjeevikumar: Center for Bioenergy and Green Engineering, Department of Energy Technology, Aalborg University, 6700 Esbjerg, Denmark
Francesco Lannuzzo: Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark
Jens Bo Holm-Nielsen: Center for Bioenergy and Green Engineering, Department of Energy Technology, Aalborg University, 6700 Esbjerg, Denmark

Energies, 2020, vol. 13, issue 6, 1-19

Abstract: In recent times, multilevel inverters are used as a high priority in many sizeable industrial drive applications. However, the reliability and performance of multilevel inverters are affected by the failure of power electronic switches. In this paper, the failure of power electronic switches of multilevel inverters is identified with the help of a high-performance diagnostic system during the open switch and low condition. Experimental and simulation analysis was carried out on five levels cascaded h-bridge multilevel inverter, and its output voltage waveforms were synthesized at different switch fault cases and different modulation index parameter values. Salient frequency-domain features of the output voltage signal were extracted using a Fast Fourier Transform decomposition technique. The real-time work of the proposed fault diagnostic system was implemented through the LabVIEW software. The Offline Artificial neural network was trained using the MATLAB software, and the overall system parameters were transferred to the LabVIEW real-time system. With the proposed method, it is possible to identify the individual faulty switch of multilevel inverters successfully.

Keywords: Artificial Neural Networks (ANN); fault diagnosis; Fast Fourier Transform (FFT); Multilevel Inverter (MLI); LabVIEW (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/13/6/1299/pdf (application/pdf)
https://www.mdpi.com/1996-1073/13/6/1299/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:13:y:2020:i:6:p:1299-:d:331297

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().