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 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