title: Fault Diagnosis Strategies for SOFC-Based Power Generation Plants.
abstract: The success of distributed power generation by plants based on solid oxide fuel cells (SOFCs) is hindered by reliability problems that can be mitigated through an effective fault detection and isolation (FDI) system. However, the numerous operating conditions under which such plants can operate and the random size of the possible faults make identifying damaged plant components starting from the physical variables measured in the plant very difficult. In this context, we assess two classical FDI strategies (model-based with fault signature matrix and data-driven with statistical classification) and the combination of them. For this assessment, a quantitative model of the SOFC-based plant, which is able to simulate regular and faulty conditions, is used. Moreover, a hybrid approach based on the random forest (RF) classification method is introduced to address the discrimination of regular and faulty situations due to its practical advantages. Working with a common dataset, the FDI performances obtained using the aforementioned strategies, with different sets of monitored variables, are observed and compared. We conclude that the hybrid FDI strategy, realized by combining a model-based scheme with a statistical classifier, outperforms the other strategies. In addition, the inclusion of two physical variables that should be measured inside the SOFCs can significantly improve the FDI performance, despite the actual difficulty in performing such measurements.
authors.affiliation: Department of Civil, Chemical and Environmental Engineering (DICCA), University of Genoa, Genova 16145, Italy. paola.costamagna@unige.it.
authors.forename: Paola
authors.initials: P
authors.lastname: Costamagna
authors.affiliation: Department of Electrical, Electronic, Telecommunications Engineering, and Naval Architecture (DITEN), University of Genoa, Genova 16145, Italy. andrea.degiorgi@edu.unige.it.
authors.forename: Andrea
authors.initials: A
authors.lastname: De Giorgi
authors.affiliation: Department of Electrical, Electronic, Telecommunications Engineering, and Naval Architecture (DITEN), University of Genoa, Genova 16145, Italy. alberto.gotelli@gmail.com.
authors.forename: Alberto
authors.initials: A
authors.lastname: Gotelli
authors.affiliation: Department of Mechanics, Energetics, Management, and Transportation (DIME), University of Genoa, Genova 16145, Italy. loredana.magistri@unige.it.
authors.forename: Loredana
authors.initials: L
authors.lastname: Magistri
authors.affiliation: Department of Electrical, Electronic, Telecommunications Engineering, and Naval Architecture (DITEN), University of Genoa, Genova 16145, Italy. gabriele.moser@unige.it.
authors.forename: Gabriele
authors.initials: G
authors.lastname: Moser
authors.affiliation: Department of Electrical, Electronic, Telecommunications Engineering, and Naval Architecture (DITEN), University of Genoa, Genova 16145, Italy. emanuelesciaccaluga@libero.it.
authors.forename: Emanuele
authors.initials: E
authors.lastname: Sciaccaluga
authors.affiliation: Department of Electrical, Electronic, Telecommunications Engineering, and Naval Architecture (DITEN), University of Genoa, Genova 16145, Italy. andrea.trucco@unige.it.|Pattern Analysis & Computer Vision (PAVIS), Istituto Italiano di Tecnologia (IIT), Genova 16163, Italy. andrea.trucco@unige.it.
authors.forename: Andrea
authors.initials: A
authors.lastname: Trucco
keywords: fault detection and isolation (FDI); model-based and data-driven strategies; pattern recognition; quantitative modelling; random forest (RF); solid oxide fuel cell (SOFC)