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research-article

Long-term NOx Emission Behavior of Heavy Duty Gas Turbines: An Approach For Model-based Monitoring and Diagnostics

[+] Author and Article Information
Moritz Lipperheide

Institute for Power Plant Technology, Steam and Gas Turbines, Department of Mechanical Engineering, RWTH Aachen University, Aachen, Germany
lipperheide@ikdg.rwth-aachen.de

Frank Weidner

Institute for Power Plant Technology, Steam and Gas Turbines, Department of Mechanical Engineering, RWTH Aachen University, Aachen, Germany
weidner@ikdg.rwth-aachen.de

Manfred Wirsum

Institute for Power Plant Technology, Steam and Gas Turbines, Department of Mechanical Engineering, RWTH Aachen University, Aachen, Germany
wirsum@ikdg.rwth-aachen.de

Martin Gassner

GE Power, Baden, Switzerland
mgassner16@gmail.com

Stefano Bernero

GE Power, Baden, Switzerland
stefano.bernero@ge.com

1Corresponding author.

ASME doi:10.1115/1.4040009 History: Received March 13, 2018; Revised April 12, 2018

Abstract

Accurate monitoring of gas turbine performance is a means to an early detection of performance deviation from the design point and thus to an optimized operational control. In this process, the diagnosis of the combustion process is of high importance due to strict legal pollution limits as aging of the combustor during operation may lead to an observed progression of NOx emissions. The method presented here features a semi-empirical NOx formulation incorporating aging for the GT24/GT26 heavy duty gas turbines: Input parameters to the NOx-correlation are processed from actual measurement data in a simplified gas turbine model. Component deterioration is accounted for by linking changes in air flow distribution and control parameters to specific operational measurements of the gas turbine. The method was validated on three different gas turbines of the GE GT24/GT26 fleet for part- and baseload operation with a total of 374,058 long-term data points (5 min average), corresponding to a total of 8.5 years of observation, while only commissioning data was used for the formulation of the NOx correlation. When input parameters to the correlation are adapted for aging, the NOx prediction outperforms the benchmark prediction method without aging by 35.9, 53.7 and 26.2 % in terms of RMSE yielding a root-mean-squared error of 1.27, 1.84 and 3.01 ppm for the investigated gas turbines over a three year monitoring period.

Copyright (c) 2018 by ASME
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