Title: Artificial neural network-based fault detection and identification in a seven-level fault-tolerant inverter

Authors: Aquib Mehdi Naqvi; Pushkar Tripathi; S.P. Singh

Addresses: Electrical Engineering Department, Institute of Engineering and Technology, Lucknow, India ' Electrical Engineering Department, Institute of Engineering and Technology, Lucknow, India ' Electrical Engineering Department, Rajkiye Engineering College, Ambedkaragar, India

Abstract: The advantages of multilevel inverters (MLIs) have led to their widespread use in various applications. However, the numerous semiconductor switches in MLIs pose reliability challenges. Researchers have developed fault-tolerant (FT) topologies and operational methods to ensure uninterrupted power supply. Detecting and identifying faults is essential for corrective measures and FT operation. This paper presents an artificial neural network (ANN)-based fault detection and identification method for a seven-level inverter. The MLI possesses high redundancy in switching states, which helps to maintain output voltage during open circuit switch faults. A five-layer ANN is trained with data of total harmonic distortion (THD) and average output voltage under different fault conditions and loads. Identifying the faulty switch allows for adjustments to the switching sequence and angles, ensuring operational continuity and optimal voltage levels. Validation of the fault detection and identification method is confirmed through simulation results.

Keywords: multilevel inverter; MLI; total harmonic distortion; THD; fault-tolerant; voltage level; neural network; switches.

DOI: 10.1504/IJAMECHS.2024.143361

International Journal of Advanced Mechatronic Systems, 2024 Vol.11 No.4, pp.192 - 199

Received: 13 Feb 2024
Accepted: 07 May 2024
Published online: 16 Dec 2024 *

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