Performance monitoring of heavy duty gas turbines based on Bayesian and Dempster-Shafer theory

Khamseh, S. Afshar; Fatehi, Alireza

doi:10.1109/eitech.2017.8255303

Cited by 1 publication

(1 citation statement)

References 21 publications

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“…It offers a stable model for gas turbine modification in temperature control and stability, load frequency control [59,62] and PID control [57]. In [63], an integration of Bayesian and Dempster-Shafer theory into Rowen's model serves as a performance monitoring tool for gas turbines. The well-known model also extended into a fault characterization study during frequency excursion.…”

Section: Black-box Modelmentioning

confidence: 99%

Dry-Low Emission Gas Turbine Technology: Recent Trends and Challenges

et al. 2022

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Dry-low emission (DLE) is one of the cleanest combustion types used in a gas turbine. DLE gas turbines have become popular due to their ability to reduce emissions by operating in lean-burn operation. However, this technology leads to challenges that sometimes interrupt regular operations. Therefore, this paper extensively reviews the development of the DLE gas turbine and its challenges. Numerous online publications from various databases, including IEEE Xplore, Scopus, and Web of Science, are compiled to describe the evolution of gas turbine technology based on emissions, fuel flexibility, and drawbacks. Various gas turbine models, including physical and black box models, are further discussed in detail. Working principles, fuel staging mechanisms, and advantages of DLE gas turbines followed by common faults that lead to gas turbine tripping are specifically discussed. A detailed evaluation of lean blow-out (LBO) as the major fault is subsequently highlighted, followed by the current methods in LBO prediction. The literature confirms that the DLE gas turbine has the most profitable features against other clean combustion methods. Simulation using Rowen’s model significantly imitates the actual behavior of the DLE gas turbine that can be used to develop a control strategy to maintain combustion stability. Lastly, the data-driven LBO prediction method helps minimize the flame’s probability of a blow-out.

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