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Incipient Fault Diagnosis of a Grid-Connected T-Type Multilevel Inverter Using Multilayer Perceptron and Walsh Transform

Author

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  • Tito G. Amaral

    (ESTSetúbal, Istituto Politécnico de Setúbal, 2914-508 Setúbal, Portugal
    Sustain.RD, IPS—Instituto Politécnico de Setúbal, 2914-508 Setúbal, Portugal)

  • Vitor Fernão Pires

    (ESTSetúbal, Istituto Politécnico de Setúbal, 2914-508 Setúbal, Portugal
    Sustain.RD, IPS—Instituto Politécnico de Setúbal, 2914-508 Setúbal, Portugal
    INESC-ID, 1000-029 Lisboa, Portugal)

  • Armando Cordeiro

    (Sustain.RD, IPS—Instituto Politécnico de Setúbal, 2914-508 Setúbal, Portugal
    INESC-ID, 1000-029 Lisboa, Portugal
    ISEL, DEEEA, IPL—Instituto Politécnico de Lisboa, 1549-020 Lisboa, Portugal)

  • Daniel Foito

    (ESTSetúbal, Istituto Politécnico de Setúbal, 2914-508 Setúbal, Portugal
    Sustain.RD, IPS—Instituto Politécnico de Setúbal, 2914-508 Setúbal, Portugal
    Department of Superior Technical School of Setúbal, Polytechnic Institute of Setúbal, CTS-UNINOVA—Universidade Nova de Lisboa, 1099-085 Lisboa, Portugal)

  • João F. Martins

    (Department of Superior Technical School of Setúbal, Polytechnic Institute of Setúbal, CTS-UNINOVA—Universidade Nova de Lisboa, 1099-085 Lisboa, Portugal
    DEE—Department of Electrical Engineering, FCT, DEEC, UNL—Universidade Nova de Lisboa, 2829-516 Lisboa, Portugal)

  • Julia Yamnenko

    (Faculty of Electronics, National Technical University of Ukraine, 03056 Kyiv, Ukraine)

  • Tetyana Tereschenko

    (Faculty of Electronics, National Technical University of Ukraine, 03056 Kyiv, Ukraine)

  • Liudmyla Laikova

    (Faculty of Electronics, National Technical University of Ukraine, 03056 Kyiv, Ukraine)

  • Ihor Fedin

    (Faculty of Electronics, National Technical University of Ukraine, 03056 Kyiv, Ukraine)

Abstract

This article deals with fault detection and the classification of incipient and intermittent open-transistor faults in grid-connected three-level T-type inverters. Normally, open-transistor detection algorithms are developed for permanent faults. Nevertheless, the difficulty to detect incipient and intermittent faults is much greater, and appropriate methods are required. This requirement is due to the fact that over time, its repetition may lead to permanent failures that may lead to irreversible degradation. Therefore, the early detection of these failures is very important to ensure the reliability of the system and avoid unscheduled stops. For diagnosing these incipient and intermittent faults, a novel method based on a Walsh transform combined with a multilayer perceptron ( MLP )-based classifier is proposed in this paper. This non-classical approach of using the Walsh transform not only allows accurate detections but is also very fast. This last characteristic is very important in these applications due to their practical implementation. The proposed method includes two main steps. First, the acquired AC currents are used by the control system and processed using the Walsh transform. This results in detailed information used to potentially identify open-transistor faults. Then, such information is processed using the MLP to finally determine whether a fault is present or not. Several experiments are conducted with different types of incipient transistor faults to create a relevant dataset.

Suggested Citation

  • Tito G. Amaral & Vitor Fernão Pires & Armando Cordeiro & Daniel Foito & João F. Martins & Julia Yamnenko & Tetyana Tereschenko & Liudmyla Laikova & Ihor Fedin, 2023. "Incipient Fault Diagnosis of a Grid-Connected T-Type Multilevel Inverter Using Multilayer Perceptron and Walsh Transform," Energies, MDPI, vol. 16(6), pages 1-18, March.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2668-:d:1095475
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    1. Mohammad Fahad & Mohd Tariq & Adil Sarwar & Mohammad Modabbir & Mohd Aman Zaid & Kuntal Satpathi & MD Reyaz Hussan & Mohammad Tayyab & Basem Alamri & Ahmad Alahmadi, 2021. "Asymmetric Multilevel Inverter Topology and Its Fault Management Strategy for High-Reliability Applications," Energies, MDPI, vol. 14(14), pages 1-21, July.
    2. Muhammad Yasir Ali Khan & Haoming Liu & Zhihao Yang & Xiaoling Yuan, 2020. "A Comprehensive Review on Grid Connected Photovoltaic Inverters, Their Modulation Techniques, and Control Strategies," Energies, MDPI, vol. 13(16), pages 1-40, August.
    3. Zheng Yin & Cungang Hu & Kui Luo & Tao Rui & Zhuangzhuang Feng & Geye Lu & Pinjia Zhang, 2022. "A Novel Model-Free Predictive Control for T-Type Three-Level Grid-Tied Inverters," Energies, MDPI, vol. 15(18), pages 1-15, September.
    4. P. Madasamy & V. Suresh Kumar & P. Sanjeevikumar & Jens Bo Holm-Nielsen & Eklas Hosain & C. Bharatiraja, 2019. "A Three-Phase Transformerless T-Type- NPC-MLI for Grid Connected PV Systems with Common-Mode Leakage Current Mitigation," Energies, MDPI, vol. 12(12), pages 1-25, June.
    5. Zhang, Cai Wen & Zhang, Tieling & Chen, Nan & Jin, Tongdan, 2013. "Reliability modeling and analysis for a novel design of modular converter system of wind turbines," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 86-94.
    6. Enrico Zio, 2013. "Monte Carlo Simulation: The Method," Springer Series in Reliability Engineering, in: The Monte Carlo Simulation Method for System Reliability and Risk Analysis, edition 127, chapter 0, pages 19-58, Springer.
    7. Enrico Zio, 2013. "The Monte Carlo Simulation Method for System Reliability and Risk Analysis," Springer Series in Reliability Engineering, Springer, edition 127, number 978-1-4471-4588-2, December.
    8. Enrico Zio, 2013. "System Reliability and Risk Analysis by Monte Carlo Simulation," Springer Series in Reliability Engineering, in: The Monte Carlo Simulation Method for System Reliability and Risk Analysis, edition 127, chapter 0, pages 59-81, Springer.
    9. Mehdi Baghli & Claude Delpha & Demba Diallo & Abdelhamid Hallouche & David Mba & Tianzhen Wang, 2019. "Three-Level NPC Inverter Incipient Fault Detection and Classification using Output Current Statistical Analysis," Energies, MDPI, vol. 12(7), pages 1-20, April.
    10. Ahmed Shawky & Mahrous Ahmed & Mohamed Orabi & Abdelali El Aroudi, 2020. "Classification of Three-Phase Grid-Tied Microinverters in Photovoltaic Applications," Energies, MDPI, vol. 13(11), pages 1-39, June.
    11. Zuo, Lin & Xu, Fengjie & Zhang, Changhua & Xiahou, Tangfan & Liu, Yu, 2022. "A multi-layer spiking neural network-based approach to bearing fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    12. Leonardo Rodriguez-Urrego & Emilio García & Eduardo Quiles & Antonio Correcher & Francisco Morant & Ricardo Pizá, 2015. "Diagnosis of Intermittent Faults in IGBTs Using the Latent Nestling Method with Hybrid Coloured Petri Nets," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-14, September.
    13. Juan Carlos Iglesias-Rojas & Erick Velázquez-Lozada & Roberto Baca-Arroyo, 2022. "Online Failure Diagnostic in Full-Bridge Module for Optimum Setup of an IGBT-Based Multilevel Inverter," Energies, MDPI, vol. 15(14), pages 1-14, July.
    14. Ying-Yi Hong & Yan-Hung Wei & Yung-Ruei Chang & Yih-Der Lee & Pang-Wei Liu, 2014. "Fault Detection and Location by Static Switches in Microgrids Using Wavelet Transform and Adaptive Network-Based Fuzzy Inference System," Energies, MDPI, vol. 7(4), pages 1-18, April.
    15. Imran Hussain & Ihsan Ullah Khalil & Aqsa Islam & Mati Ullah Ahsan & Taosif Iqbal & Md. Shahariar Chowdhury & Kuaanan Techato & Nasim Ullah, 2022. "Unified Fuzzy Logic Based Approach for Detection and Classification of PV Faults Using I-V Trend Line," Energies, MDPI, vol. 15(14), pages 1-14, July.
    16. Enrico Zio, 2013. "System Reliability and Risk Analysis," Springer Series in Reliability Engineering, in: The Monte Carlo Simulation Method for System Reliability and Risk Analysis, edition 127, chapter 0, pages 7-17, Springer.
    17. Arturo Y. Jaen-Cuellar & David A. Elvira-Ortiz & Roque A. Osornio-Rios & Jose A. Antonino-Daviu, 2022. "Advances in Fault Condition Monitoring for Solar Photovoltaic and Wind Turbine Energy Generation: A Review," Energies, MDPI, vol. 15(15), pages 1-36, July.
    18. Park, Chan Hee & Kim, Hyeongmin & Suh, Chaehyun & Chae, Minseok & Yoon, Heonjun & Youn, Byeng D., 2022. "A health image for deep learning-based fault diagnosis of a permanent magnet synchronous motor under variable operating conditions: Instantaneous current residual map," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
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