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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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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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.

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