Fault diagnosis in a five-level multilevel inverter using an artificial neural network approach





artificial neural network, fast Fourier transform, multilevel inverter, sinusoidal pulse width modulation, total harmonic distortion


Introduction. Cascaded H-bridge multilevel inverters (CHB-MLI) are becoming increasingly used in applications such as distribution systems, electrical traction systems, high voltage direct conversion systems, and many others. Despite the fact that multilevel inverters contain a large number of control switches, detecting a malfunction takes a significant amount of time. In the fault switch configurations diode included for freewheeling operation during open-fault condition. During short circuit fault conditions are carried out by the fuse, which can reveal the freewheeling current direction. The fault category can be identified independently and also failure of power switches harmed by the functioning and reliability of CHB-MLI. This paper investigates the effects and performance of open and short switching faults of multilevel inverters. Output voltage characteristics of 5 level MLI are frequently determined from distinctive switch faults with modulation index value of 0.85 is used during simulation analysis. In the simulation experiment for the modulation index value of 0.85, one second open and short circuit faults are created for the place of faulty switch. Fault is identified automatically by means of artificial neural network (ANN) technique using sinusoidal pulse width modulation based on distorted total harmonic distortion (THD) and managed by its own. The novelty of the proposed work consists of a fast Fourier transform (FFT) and ANN to identify faulty switch. Purpose. The proposed architecture is to identify faulty switch during open and short failures, which has to be reduced THD and make the system in reliable operation. Methods. The proposed topology is to be design and evaluate using MATLAB/Simulink platform. Results. Using the FFT and ANN approaches, the normal and faulty conditions of the MLI are explored, and the faulty switch is detected based on voltage changing patterns in the output. Practical value. The proposed topology has been very supportive for implementing non-conventional energy sources based multilevel inverter, which is connected to large demand in grid.

Author Biographies

E. Parimalasundar, Sree Vidyanikethan Engineering College

Associate Professor, Department of Electrical & Electronics Engineering

R. Senthil Kumar, SRM Institute of Science and Technology

Assistant Professor, Department of Electrical and Electronics Engineering

V. S. Chandrika, KPR Institute of Engineering and Technology

Professor, Department of Electrical and Electronics Engineering

K. Suresh, Christ (Deemed to be University)

Associate Professor, Department of Electrical and Electronics Engineering


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How to Cite

Parimalasundar, E., Senthil Kumar, R., Chandrika, V. S., & Suresh, K. (2023). Fault diagnosis in a five-level multilevel inverter using an artificial neural network approach. Electrical Engineering & Electromechanics, (1), 31–39. https://doi.org/10.20998/2074-272X.2023.1.05



Industrial Electronics