LEAN BLOWOUT FAULT PREDICTION FOR DRY LOW EMISSION GAS TURBINE USING HYBRID OF SUPPORT VECTOR MACHINE AND BAYESIAN BELIEF NETWORK
A Dry-Low Emission (DLE) gas turbine reduces Carbon Oxide (COx) and Nitrogen Oxide (NOx) emission during power generation. However, DLE gas turbines frequently encounter trips due to Lean Blowout (LBO) fault. The state-of-the-art studies on LBO are performed in a laboratory-scale where gas turbin...
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my-utp-utpedia.207272021-09-08T16:03:43Z http://utpedia.utp.edu.my/20727/ LEAN BLOWOUT FAULT PREDICTION FOR DRY LOW EMISSION GAS TURBINE USING HYBRID OF SUPPORT VECTOR MACHINE AND BAYESIAN BELIEF NETWORK OMAR, MADIAH TK Electrical engineering. Electronics Nuclear engineering A Dry-Low Emission (DLE) gas turbine reduces Carbon Oxide (COx) and Nitrogen Oxide (NOx) emission during power generation. However, DLE gas turbines frequently encounter trips due to Lean Blowout (LBO) fault. The state-of-the-art studies on LBO are performed in a laboratory-scale where gas turbine dynamics are not well represented. There is a potential of utilizing a dynamic model where DLE gas turbine model is developed to predict LBO fault. However, the superior prediction technique such as Support Vector Machine (SVM) is deterministic without the probability of the impending trip. Therefore, this thesis proposes a DLE gas turbine model with a hybrid of Support Vector Machine-Bayesian Belief Network (SVM-BBN) for LBO fault prediction. 2021-01 Thesis NonPeerReviewed application/pdf en http://utpedia.utp.edu.my/20727/1/Madiah%20Omar_G03268.pdf OMAR, MADIAH (2021) LEAN BLOWOUT FAULT PREDICTION FOR DRY LOW EMISSION GAS TURBINE USING HYBRID OF SUPPORT VECTOR MACHINE AND BAYESIAN BELIEF NETWORK. PhD thesis, Universiti Teknologi PETRONAS. |
| institution |
Universiti Teknologi Petronas |
| building |
UTP Resource Centre |
| collection |
Institutional Repository |
| continent |
Asia |
| country |
Malaysia |
| content_provider |
Universiti Teknologi Petronas |
| content_source |
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| url_provider |
http://utpedia.utp.edu.my/ |
| language |
English |
| topic |
TK Electrical engineering. Electronics Nuclear engineering |
| spellingShingle |
TK Electrical engineering. Electronics Nuclear engineering OMAR, MADIAH LEAN BLOWOUT FAULT PREDICTION FOR DRY LOW EMISSION GAS TURBINE USING HYBRID OF SUPPORT VECTOR MACHINE AND BAYESIAN BELIEF NETWORK |
| description |
A Dry-Low Emission (DLE) gas turbine reduces Carbon Oxide (COx) and Nitrogen
Oxide (NOx) emission during power generation. However, DLE gas turbines
frequently encounter trips due to Lean Blowout (LBO) fault. The state-of-the-art
studies on LBO are performed in a laboratory-scale where gas turbine dynamics are
not well represented. There is a potential of utilizing a dynamic model where DLE gas
turbine model is developed to predict LBO fault. However, the superior prediction
technique such as Support Vector Machine (SVM) is deterministic without the
probability of the impending trip. Therefore, this thesis proposes a DLE gas turbine
model with a hybrid of Support Vector Machine-Bayesian Belief Network
(SVM-BBN) for LBO fault prediction. |
| format |
Thesis |
| author |
OMAR, MADIAH |
| author_facet |
OMAR, MADIAH |
| author_sort |
OMAR, MADIAH |
| title |
LEAN BLOWOUT FAULT PREDICTION FOR DRY LOW EMISSION GAS
TURBINE USING HYBRID OF SUPPORT VECTOR MACHINE AND BAYESIAN
BELIEF NETWORK |
| title_short |
LEAN BLOWOUT FAULT PREDICTION FOR DRY LOW EMISSION GAS
TURBINE USING HYBRID OF SUPPORT VECTOR MACHINE AND BAYESIAN
BELIEF NETWORK |
| title_full |
LEAN BLOWOUT FAULT PREDICTION FOR DRY LOW EMISSION GAS
TURBINE USING HYBRID OF SUPPORT VECTOR MACHINE AND BAYESIAN
BELIEF NETWORK |
| title_fullStr |
LEAN BLOWOUT FAULT PREDICTION FOR DRY LOW EMISSION GAS
TURBINE USING HYBRID OF SUPPORT VECTOR MACHINE AND BAYESIAN
BELIEF NETWORK |
| title_full_unstemmed |
LEAN BLOWOUT FAULT PREDICTION FOR DRY LOW EMISSION GAS
TURBINE USING HYBRID OF SUPPORT VECTOR MACHINE AND BAYESIAN
BELIEF NETWORK |
| title_sort |
lean blowout fault prediction for dry low emission gas
turbine using hybrid of support vector machine and bayesian
belief network |
| publishDate |
2021 |
| url |
http://utpedia.utp.edu.my/20727/1/Madiah%20Omar_G03268.pdf http://utpedia.utp.edu.my/20727/ |
| _version_ |
1739832789268692992 |
| score |
13.251813 |