TY - GEN
T1 - Fault Detection and Classification in Multilevel Inverter using Machine Learning
AU - Jeevan, N. D.
AU - Dewangan, Niraj Kumar
AU - Karthik, B. M.
AU - Gupta, Krishna Kumar
AU - Singh, Vikram
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - Multilevel inverters (MLIs) are predominantly employed in commercial applications that require high voltages and powers. As the switch count in a system increases, the probability of failure of a multilevel inverter increases. It is essential to identify switch faults in power converters. This study exclusively examined open-circuit switch faults in MLI. The proposed Machine Learning (ML) based open-switch fault diagnosis method employs only the output voltage data. Three features were extracted from the output voltage. The ML methods employed such as Decision Trees (DT), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Random Forests (RF). The proposed diagnostic approach was implemented in the MATLAB/Simulink environment. KNN achieved a maximum accuracy of 99.52% with a training and testing data split of 70% and 30% respectively.
AB - Multilevel inverters (MLIs) are predominantly employed in commercial applications that require high voltages and powers. As the switch count in a system increases, the probability of failure of a multilevel inverter increases. It is essential to identify switch faults in power converters. This study exclusively examined open-circuit switch faults in MLI. The proposed Machine Learning (ML) based open-switch fault diagnosis method employs only the output voltage data. Three features were extracted from the output voltage. The ML methods employed such as Decision Trees (DT), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Random Forests (RF). The proposed diagnostic approach was implemented in the MATLAB/Simulink environment. KNN achieved a maximum accuracy of 99.52% with a training and testing data split of 70% and 30% respectively.
UR - https://www.scopus.com/pages/publications/105016783438
UR - https://www.scopus.com/inward/citedby.url?scp=105016783438&partnerID=8YFLogxK
U2 - 10.1109/APCI65531.2025.11137326
DO - 10.1109/APCI65531.2025.11137326
M3 - Conference contribution
AN - SCOPUS:105016783438
T3 - APCI 2025 - 2025 International Conference on Advancements in Power, Communication and Intelligent Systems
BT - APCI 2025 - 2025 International Conference on Advancements in Power, Communication and Intelligent Systems
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Conference on Advancements in Power, Communication and Intelligent Systems, APCI 2025
Y2 - 27 June 2025 through 28 June 2025
ER -
