Long-Term NO_x Emission Behavior of Heavy Duty Gas Turbines: An Approach for Model-Based Monitoring and Diagnostics

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 NO_x emissions. The method presented here features a semi-empirical NO_x formulation incorporating aging for the GT24/GT26 heavy duty gas turbines: Input parameters to the NO_x-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 were used for the formulation of the NO_x correlation. When input parameters to the correlation are adapted for aging, the NO_x prediction outperforms the benchmark prediction method without aging by 35.9, 53.7, and 26.2% in terms of root mean square error (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.