Artificial Neural Networks Based Parametric Curve Generation for Health Assessment of Industrial Gas Turbine Systems - Raghavan, Shaiju M.; Palatel, Arun; Simon, Jayaraj (Process integration and optimization for sustainability, 2024)
Degradation assessment of gas turbine components in ‘off-design’ operating conditions is one of the main concerns of gas turbine users. This paper proposes a systematic methodology to promptly identify the fast degradation of gas turbine module components under any specified operating condition based on operational data of the machine. It is attempted to segregate the performance deviation due to part-load operation and due to other degradation effects, taking the case of typical General Electric Frame 9E machine components used in a combined cycle power plant in India. The development of artificial neural network (ANN) models of inlet filtration system, compressor, combustion chamber and turbine based on process history data, covering 60 to 100% load operation is described initially. The methodology for generation of parametric curves for performance assessment of individual components is illustrated further. It is shown that the sufficiently trained ANN models ( R 2 > 0.95) can be used for performance assessment of inlet filtration system, identification of fast rate of compressor fouling, combustion nozzle crack discovery and turbine angel wing seal damage. The degradation assessment tool assisted in the identification of the necessity for rectification works such as inlet filter replacement, compressor wash, combustion inspection and hot gas path inspection. The residuals of critical parameters identified in the study are found to be helpful in estimating the performance of components after machine overhauling. The proposed steady-state operational data based generic algorithm can be applied for the performance assessment of any similar gas turbine power plant.