A Classification Approach for Model-Based Fault Diagnosis in Power Generation Systems Based on Solid Oxide Fuel Cells

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.