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A Classification Approach for Model-Based Fault Diagnosis in Power Generation Systems Based on Solid Oxide Fuel Cells.
- Source :
- IEEE Transactions on Energy Conversion; Jun2016, Vol. 31 Issue 2, p676-687, 12p
- Publication Year :
- 2016
-
Abstract
- Solid oxide fuel cells (SOFCs) are a promising option for power generation plants, but the design of fault diagnosis methods remains a key challenge. We propose the use of a quantitative model for such a plant (validated by real experiments) with a support vector machine (SVM) to detect and classify possible faults. The adoption of a classification approach as an identification strategy in a model-based fault diagnosis process represents a major innovation in the field of SOFC plants. Constant–voltage and constant–current control strategies are investigated. In both cases, an adequately trained SVM classifier is used to provide a high probability of correct classification when the plant functions at different steady-state operating conditions for random sizes of the considered faults and for realistic magnitudes of the errors affecting the model predictions. In addition, the relative importance of the easy-to-measure residuals, which are used as features in the SVM classification process, are discussed based on an advanced feature selection technique. [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 08858969
- Volume :
- 31
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Energy Conversion
- Publication Type :
- Academic Journal
- Accession number :
- 115559894
- Full Text :
- https://doi.org/10.1109/TEC.2015.2492938