Sökning: onr:"swepub:oai:DiVA.org:mdh-54411" >
A Degradation Diagn...
A Degradation Diagnosis Method for Gas Turbine-Fuel Cell Hybrid Systems Using Bayesian Networks
-
- Mantel, Luca (författare)
- Univ Genoa, TPG DIME, Via Montallegro 1, I-16145 Genoa, Italy.
-
- Zaccaria, Valentina, 1989- (författare)
- Mälardalens högskola,Framtidens energi
-
- Ferrari, Mario Luigi (författare)
- Univ Genoa, TPG DIME, Via Montallegro 1, I-16145 Genoa, Italy.
-
visa fler...
-
- Kyprianidis, Konstantinos (författare)
- Mälardalens högskola,Framtidens energi
-
visa färre...
-
Univ Genoa, TPG DIME, Via Montallegro 1, I-16145 Genoa, Italy Framtidens energi (creator_code:org_t)
- 2021-03-15
- 2021
- Engelska.
-
Ingår i: Journal of engineering for gas turbines and power. - : ASME. - 0742-4795 .- 1528-8919. ; 143:5
- Relaterad länk:
-
https://urn.kb.se/re...
-
visa fler...
-
https://doi.org/10.1...
-
visa färre...
Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Maskinteknik -- Energiteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Mechanical Engineering -- Energy Engineering (hsv//eng)
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
Hitta via bibliotek
Till lärosätets databas