Validation of neural network-based fault diagnosis for multi-stack fuel cell systems: Stack voltage deviation detection

Antti Pohjoranta, Marco Sorrentino (Corresponding Author), Cesare Pianese, F. Amatruda, Tero Hottinen

    Research output: Contribution to journalArticleScientificpeer-review

    10 Citations (Scopus)

    Abstract

    This paper presents (i) an algorithm for the detection of unexpected stack voltage deviations in an Solid Oxide Fuel Cells (SOFC)-based power system with multiple stacks and (ii) its validation in a simulated online environment. The algorithm is based on recurrent neural networks (RNNs) and is validated by using operating data from the Wärtsilä WFC20 multi-stack SOFC system. The voltage deviation detection is based on statistical testing. Instead of a hardware implementation in the actual power plant, the algorithm is validated in a simulated online environment that provides data I/O communication based on the OPC (i.e. Object Linking and Embedding (OLE) for Process Control) protocol, which is also the technology utilized in the real hardware environment. The validation tests show that the RNN-based algorithm effectively detects unwanted stack voltage deviations and also that it is online-viable.
    Original languageEnglish
    Pages (from-to)173-181
    JournalEnergy Procedia
    Volume81
    DOIs
    Publication statusPublished - 2015
    MoE publication typeA1 Journal article-refereed
    Event69th Conference of the Italian Thermal Machines Engineering Association, ATI 2014 - Milan, Italy
    Duration: 10 Sep 201412 Sep 2014

    Fingerprint

    Failure analysis
    Fuel cells
    Neural networks
    Recurrent neural networks
    Electric potential
    Solid oxide fuel cells (SOFC)
    Hardware
    Process control
    Power plants
    Communication
    Testing

    Keywords

    • solid oxide fuel cell
    • multi-stack system
    • voltage fault diagnosis
    • online validation
    • neural networks

    Cite this

    Pohjoranta, Antti ; Sorrentino, Marco ; Pianese, Cesare ; Amatruda, F. ; Hottinen, Tero. / Validation of neural network-based fault diagnosis for multi-stack fuel cell systems : Stack voltage deviation detection. In: Energy Procedia. 2015 ; Vol. 81. pp. 173-181.
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    title = "Validation of neural network-based fault diagnosis for multi-stack fuel cell systems: Stack voltage deviation detection",
    abstract = "This paper presents (i) an algorithm for the detection of unexpected stack voltage deviations in an Solid Oxide Fuel Cells (SOFC)-based power system with multiple stacks and (ii) its validation in a simulated online environment. The algorithm is based on recurrent neural networks (RNNs) and is validated by using operating data from the W{\"a}rtsil{\"a} WFC20 multi-stack SOFC system. The voltage deviation detection is based on statistical testing. Instead of a hardware implementation in the actual power plant, the algorithm is validated in a simulated online environment that provides data I/O communication based on the OPC (i.e. Object Linking and Embedding (OLE) for Process Control) protocol, which is also the technology utilized in the real hardware environment. The validation tests show that the RNN-based algorithm effectively detects unwanted stack voltage deviations and also that it is online-viable.",
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    author = "Antti Pohjoranta and Marco Sorrentino and Cesare Pianese and F. Amatruda and Tero Hottinen",
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    Validation of neural network-based fault diagnosis for multi-stack fuel cell systems : Stack voltage deviation detection. / Pohjoranta, Antti; Sorrentino, Marco (Corresponding Author); Pianese, Cesare; Amatruda, F.; Hottinen, Tero.

    In: Energy Procedia, Vol. 81, 2015, p. 173-181.

    Research output: Contribution to journalArticleScientificpeer-review

    TY - JOUR

    T1 - Validation of neural network-based fault diagnosis for multi-stack fuel cell systems

    T2 - Stack voltage deviation detection

    AU - Pohjoranta, Antti

    AU - Sorrentino, Marco

    AU - Pianese, Cesare

    AU - Amatruda, F.

    AU - Hottinen, Tero

    PY - 2015

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    AB - This paper presents (i) an algorithm for the detection of unexpected stack voltage deviations in an Solid Oxide Fuel Cells (SOFC)-based power system with multiple stacks and (ii) its validation in a simulated online environment. The algorithm is based on recurrent neural networks (RNNs) and is validated by using operating data from the Wärtsilä WFC20 multi-stack SOFC system. The voltage deviation detection is based on statistical testing. Instead of a hardware implementation in the actual power plant, the algorithm is validated in a simulated online environment that provides data I/O communication based on the OPC (i.e. Object Linking and Embedding (OLE) for Process Control) protocol, which is also the technology utilized in the real hardware environment. The validation tests show that the RNN-based algorithm effectively detects unwanted stack voltage deviations and also that it is online-viable.

    KW - solid oxide fuel cell

    KW - multi-stack system

    KW - voltage fault diagnosis

    KW - online validation

    KW - neural networks

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