A CLASSIFICATION APPROACH FOR MODEL BASED FAULT DIAGNOSIS IN POWER SYSTEMS BY USING SOLID OXIDE FUEL CELLS

D.SIRI CHANDANA
Student, Dept. of EEE,NETW , IN

ESLAVATH MOUNIKA
Student, Dept. of EEE,NETW , IN

JARAPALA SIVANAIK
Student, Dept. of EEE,NETW , IN

MALOTH HUSSEN
Student, Dept. of EEE,NETW , IN

Keywords: Fault Detection and Isolation(FDI) and Solid oxide fuel cells (SOFC) and feature selection Support Vector machine(SVM).

Abstract

In this paper, the authors present an overview for modeling of solid oxide fuel cells. Even though, a large amount of literature of sofc’s has been published in the last decade, most of them has focused on electro chemical characteristics.

To determine the fault diagnosis in power generation systems by using solid oxide fuel cells. We propose the use of quantitative model for such a plant with a support vector machine(SVM) to detect and to classify possible faults. Solid oxide fuel cells(SOFC’s) are the future of the energy .In addition, the relative importance of the easy to measure residuals which are used as feature in the SVM classificationprocess,are discussed based on an advanced feature selection technique.

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