Work

Intelligent Fault Localization of Switches in Multilevel Inverter

Year: 2026

Type: article

Abstract: Multilevel inverters (MLIs) based on IGBT switches have gained prominence in AC power applications due to their capability to reduce harmonic distortion while offering cost‐effective operation. They are widely adopted in power electronic systems; however, under high‐stress conditions, power switches are prone to faults that can impair system performance. Hence, effective identification of faulty switches is crucial. This study focuses on detecting single and multiple switch open‐circuit faults (OCFs) in reduced device count (RDC) MLI. A machine learning (ML)–based diagnostic framework is proposed, which utilizes only the output voltage signals for fault analysis. From these signals, three key features are extracted: standard deviation, half‐cycle moving average, and total harmonic distortion for fault classification. Several ML classifiers are evaluated and benchmarked against recent approaches, with the decision tree (DT) model achieving the highest accuracy of 99.84% under a 70:30 training‐to‐testing split. The proposed method accurately identified both single and multiple switch OCFs in RDC‐MLI within 10–30 ms. The complete diagnostic system is implemented and validated in the MATLAB/Simulink environment.

Source: International Transactions on Electrical Energy Systems

DOI: 10.1155/etep/1148639

Authors: Jeevan N. D., Niraj Kumar Dewangan, Karthik B. M., Krishna Kumar Gupta, Abhinandan Routray, Anita Khanna

Institutions: Manipal Academy of Higher Education, Thapar Institute of Engineering & Technology, Guru Ghasidas Vishwavidyalaya

Language: English

Topic: Multilevel Inverters and Converters

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy

Open Access status: gold

Estimated APC paid: $2,350