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Model-Based Data Driven Approach for Fault Identification in Proton Exchange Membrane Fuel Cell

Author

Listed:
  • K. V. S. Bharath

    (Advance Power Electronics Research Lab, Department of Electrical Engineering, Jamia Millia Islamia, New Delhi 110025, India)

  • Frede Blaabjerg

    (Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark)

  • Ahteshamul Haque

    (Advance Power Electronics Research Lab, Department of Electrical Engineering, Jamia Millia Islamia, New Delhi 110025, India)

  • Mohammed Ali Khan

    (Advance Power Electronics Research Lab, Department of Electrical Engineering, Jamia Millia Islamia, New Delhi 110025, India)

Abstract

This paper develops a model-based data driven algorithm for fault classification in proton exchange membrane fuel cells (PEMFCs). The proposed approach overcomes the drawbacks of voltage and current density assumptions in conventional model-based fault identification methods and data limitations in existing data driven approaches. This is achieved by developing a 3D model of fuel cells (FC) based on semi empirical model, analytical representation of electrochemical model, thermal model, and impedance model. The developed model is simulated for membrane drying and flooding faults in PEMFC and their effects are identified for the action of varying temperature, pressure, and relative humidity. The ohmic, concentration, activation and cell voltage losses for the simulated faults are observed and processed with wavelet transforms for feature extraction. Furthermore, the support vector machine learning algorithm is adapted to develop the proposed fault classification approach. The performance of the developed classifier is tested for an unknown data and calibrated through classification accuracy. The results showed 95.5% training efficiency and 98.6% testing efficiency.

Suggested Citation

  • K. V. S. Bharath & Frede Blaabjerg & Ahteshamul Haque & Mohammed Ali Khan, 2020. "Model-Based Data Driven Approach for Fault Identification in Proton Exchange Membrane Fuel Cell," Energies, MDPI, vol. 13(12), pages 1-18, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:12:p:3144-:d:372791
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    References listed on IDEAS

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    5. Varaha Satya Bharath Kurukuru & Frede Blaabjerg & Mohammed Ali Khan & Ahteshamul Haque, 2020. "A Novel Fault Classification Approach for Photovoltaic Systems," Energies, MDPI, vol. 13(2), pages 1-17, January.
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    Cited by:

    1. Khadijeh Hooshyari & Bahman Amini Horri & Hamid Abdoli & Mohsen Fallah Vostakola & Parvaneh Kakavand & Parisa Salarizadeh, 2021. "A Review of Recent Developments and Advanced Applications of High-Temperature Polymer Electrolyte Membranes for PEM Fuel Cells," Energies, MDPI, vol. 14(17), pages 1-38, September.
    2. Guarino, Antonio & Trinchero, Riccardo & Canavero, Flavio & Spagnuolo, Giovanni, 2022. "A fast fuel cell parametric identification approach based on machine learning inverse models," Energy, Elsevier, vol. 239(PC).
    3. Antoine Bäumler & Jianwen Meng & Abdelmoudjib Benterki & Toufik Azib & Moussa Boukhnifer, 2023. "A System-Level Modeling of PEMFC Considering Degradation Aspect towards a Diagnosis Process," Energies, MDPI, vol. 16(14), pages 1-19, July.
    4. Shin, Donghoon & Yoo, Seungryeol, 2023. "Diagnostic method for PEM fuel cell states using probability Distribution-Based loss component analysis for voltage loss decomposition," Applied Energy, Elsevier, vol. 330(PB).

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