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

Author

Listed:
  • 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
    Department of Electrical Engineering, Ecole de Technologie Supérieure, Montreal, QC H3C 1K3, Canada)

  • Nazih Moubayed

    (Faculty of Engineering, Lebanese University, CRSI, LaRGES, Tripoli 1300, Lebanon)

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.

Suggested Citation

  • Bushra Masri & Hiba Al Sheikh & Nabil Karami & Hadi Y. Kanaan & Nazih Moubayed, 2025. "A Comparative Analysis of Artificial Intelligence Techniques for Single Open-Circuit Fault Detection in a Packed E-Cell Inverter," Energies, MDPI, vol. 18(6), pages 1-26, March.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:6:p:1312-:d:1607300
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    References listed on IDEAS

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    1. Eirik Odinsen & Mahshid N. Amiri & Odne S. Burheim & Jacob J. Lamb, 2024. "Estimation of Differential Capacity in Lithium-Ion Batteries Using Machine Learning Approaches," Energies, MDPI, vol. 17(19), pages 1-15, October.
    2. Da Zhang & Shuailin Chen, 2021. "Insulator Contamination Grade Recognition Using the Deep Learning of Color Information of Images," Energies, MDPI, vol. 14(20), pages 1-15, October.
    3. Yuanyuan Yang & Md Muhie Menul Haque & Dongling Bai & Wei Tang, 2021. "Fault Diagnosis of Electric Motors Using Deep Learning Algorithms and Its Application: A Review," Energies, MDPI, vol. 14(21), pages 1-26, October.
    4. Li Ding & Haotian Guo & Liqiang Bian, 2024. "Convolutional Neural Networks Based on Resonance Demodulation of Vibration Signal for Rolling Bearing Fault Diagnosis in Permanent Magnet Synchronous Motors," Energies, MDPI, vol. 17(17), pages 1-20, August.
    5. Mehdi Syed Musadiq & Dong-Myung Lee, 2024. "A Novel Capacitance Estimation Method of Modular Multilevel Converters for Motor Drives Using Recurrent Neural Networks with Long Short-Term Memory," Energies, MDPI, vol. 17(22), pages 1-17, November.
    6. Linh Bui Duy & Ninh Nguyen Quang & Binh Doan Van & Eleonora Riva Sanseverino & Quynh Tran Thi Tu & Hang Le Thi Thuy & Sang Le Quang & Thinh Le Cong & Huyen Cu Thi Thanh, 2024. "Refining Long Short-Term Memory Neural Network Input Parameters for Enhanced Solar Power Forecasting," Energies, MDPI, vol. 17(16), pages 1-22, August.
    7. Arailym Serikbay & Mehdi Bagheri & Amin Zollanvari & B. T. Phung, 2024. "Ensemble Pretrained Convolutional Neural Networks for the Classification of Insulator Surface Conditions," Energies, MDPI, vol. 17(22), pages 1-17, November.
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