Abstract
Accurate component analytical solution is very important to gas path prognostics and diagnostics of a gas turbine. However, due to the highly complex nonlinear behavior of component performance, the nonlinear relationships between various key parameters still should be further studied. For this purpose, a new component analytical solution is proposed to enhance the current adaptation and diagnostics scheme of gas turbines. First, the tuning factors are defined to construct the enhanced component analytical solution and identify the nonlinear behaviors more accurately. Second, a sensitivity analysis for tuning factors is performed to understand the effect of each factor on the shape of component maps. Then, a particle swarm optimization algorithm is used to capture the optimal tuning factors, and then the performance adaptation is implemented. Finally, the proposed method has been validated with normal field data and fouling fault field data of a PGT25+ gas turbine. Compared with two earlier off-design point adaptation methods, the proposed method shows some advantages in performance adaptation and diagnostics.
Highlights
We have discovered that the constructed tuning factors could produce special adaptive characteristics for performance adaptation of gas turbines
enhanced component analytical solution (ECAS) based on component analytical solution and the extracted tuning factors has more complicated expressions concerning the physical process of the gas turbine, which could match the shape of component maps in the whole speed region
The proposed ECAS method could remain the appropriate nonlinear matching characteristics of the component analytical solution in all speed regions, and simultaneously further reduce the deviation caused by manufacturing, assembly tolerance, and overhaul of gas turbines
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The approaches in the second area can optimize the component maps by newly constructed mathematic models, and the simulated errors at off-design points of the middle-speed region were reduced considerably. The component analytical solution could match the shape of component maps in the whole speed region, the deviation caused by manufacturing, assembly tolerance, overhaul of gas turbines, and the physical process of components has not been well considered. ECAS based on component analytical solution and the extracted tuning factors has more complicated expressions concerning the physical process of the gas turbine, which could match the shape of component maps in the whole speed region. The proposed ECAS method could remain the appropriate nonlinear matching characteristics of the component analytical solution in all speed regions, and simultaneously further reduce the deviation caused by manufacturing, assembly tolerance, and overhaul of gas turbines. The proposed two methods have good performance in the field data analysis and realize fault diagnostics
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