During the last decades there has been a rise of awareness regarding the necessity to increase energy systems efficiency and reduce carbon emissions. These goals could be partially achieved through a greater use of gas turbine - solid oxide fuel cell hybrid systems to generate both electric power and heat. However, this kind of systems are known to be delicate, especially due to the fragility of the cell, which could be permanently damaged if its temperature and pressure levels exceed their operative limits. This could be caused by degradation of a component in the system (e.g. the turbomachinery), but also by some sensor fault which leads to a wrong control action. To be considered commercially competitive, these systems must guarantee high reliability and their maintenance costs must be minimized. Thus, it is necessary to integrate these plants with an automated diagnosis system capable to detect degradation levels of the many components (e.g. turbomachinery and fuel cell stack) in order to plan properly the maintenance operations, and also to recognize a sensor fault. This task can be very challenging due to the high complexity of the system and the interactions between its components. Another difficulty is related to the lack of sensors, which is common on commercial power plants, and makes harder the identification of faults in the system.

This paper aims to develop and test a Bayesian belief network based diagnosis method, which could be used to predict the most likely degradation levels of turbine, compressor and fuel cell in a hybrid system on the basis of different sensors measurements. The capability of the diagnosis system to understand if an abnormal measurement is caused by a component degradation or by a sensor fault will be also investigated. The data used both to train and to test the network will be generated from a hybrid system deterministic model and later modified to consider noise or bias in the sensors. The application of Bayesian belief networks to gas turbine – fuel cell hybrid systems is novel, thus this article could be a significant starting point to understand their potential. The diagnosis system developed for this work is expected to provide essential information regarding levels of degradation and presence of faults. This information could be also integrated with the control system to achieve safer and more efficient operations of these plants.


A Degradation Diagnosis Method for Gas Turbine \u2013 Fuel Cell Hybrid Systems Using Bayesian Networks

Division / Sponsor:
International Gas Turbine Institute (IGTI)

Paper Proceedings: https://asmedigitalcollection.asme.org/GT/GT2020/volume/84058

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