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- Mantel, Luca, et al.
(author)
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A Degradation Diagnosis Method for Gas Turbine-Fuel Cell Hybrid Systems Using Bayesian Networks
- 2021
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In: Journal of engineering for gas turbines and power. - : ASME. - 0742-4795 .- 1528-8919. ; 143:5
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Journal article (peer-reviewed)abstract
- This paper aims to develop and test Bayesian belief network-based diagnosis methods, which can be used to predict the most likely degradation levels of turbine, compressor, and fuel cell (FC) in a hybrid system based on different sensors measurements. The capability of the diagnosis systems to understand if an abnormal measurement is caused by a component degradation or by a sensor fault is also investigated. The data used both to train and to test the networks are generated from a deterministic model and later modified to consider noise or bias in the sensors. The application of Bayesian belief networks (BBNs) to fuel cell-gas turbine hybrid systems is novel, thus the results obtained from this analysis could be a significant starting point to understand their potential. The diagnosis systems developed for this work provide essential information regarding levels of degradation and presence of faults in a gas turbine, fuel cell and sensors in a fuel cell-gas turbine hybrid system. The Bayesian belief networks proved to have a good level of accuracy for all the scenarios considered, regarding both steady-state and transient operations. This analysis also suggests that in the future a Bayesian belief network could be integrated with the control system to achieve safer and more efficient operations of these plants.
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