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)


    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
    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



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

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