A Comparative Analysis of Artificial Intelligence Techniques for Single Open-Circuit Fault Detection in a Packed E-Cell Inverter

Bushra Masri, Hiba Al Sheikh, Nabil Karami, Hadi Y. Kanaan () and Nazih Moubayed
Additional contact information
Bushra Masri: Faculty of Engineering and Architecture—ESIB, Saint-Joseph University of Beirut, Beirut 1104 2020, Lebanon
Hiba Al Sheikh: Faculty of Engineering and Information Technology, City University, Tripoli 1300, Lebanon
Nabil Karami: Faculty of Engineering Technology and Science, Higher Colleges of Technology, Dubai 341041, United Arab Emirates
Hadi Y. Kanaan: Faculty of Engineering and Architecture—ESIB, Saint-Joseph University of Beirut, Beirut 1104 2020, Lebanon
Nazih Moubayed: Faculty of Engineering, Lebanese University, CRSI, LaRGES, Tripoli 1300, Lebanon

Energies, 2025, vol. 18, issue 6, 1-26

Abstract: Recently, fault detection has played a crucial role in ensuring the safety and reliability of inverter operation. Switch failures are primarily classified into Open-Circuit (OC) and short-circuit faults. While OC failures have limited negative impacts, prolonged system operation under such conditions may lead to further malfunctions. This paper demonstrates the effectiveness of employing Artificial Intelligence (AI) approaches for detecting single OC faults in a Packed E-Cell (PEC) inverter. Two promising strategies are considered: Random Forest Decision Tree (RFDT) and Feed-Forward Neural Network (FFNN). A comprehensive literature review of various fault detection approaches is first conducted. The PEC inverter’s modulation scheme and the significance of OC fault detection are highlighted. Next, the proposed methodology is introduced, followed by an evaluation based on five performance metrics, including an in-depth comparative analysis. This paper focuses on improving the robustness of fault detection strategies in PEC inverters using MATLAB/Simulink software. Simulation results show that the RFDT classifier achieved the highest accuracy of 93%, the lowest log loss value of 0.56, the highest number of correctly predicted estimations among the total samples, and nearly perfect ROC and PR curves, demonstrating exceptionally high discriminative ability across all fault categories.

Keywords: wavelet analysis; feature extraction; switching faults; random forest decision tree; feed-forward neural network; fault detection (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: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/6/1312/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/6/1312/ (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:18:y:2025:i:6:p:1312-:d:1607300

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 ().