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 36.7, 54.0 and 26.7 % in terms of RMSE yielding a root-mean-squared error of 1.26, 1.81 and 2.99 ppm for the investigated gas turbines over a three year monitoring period.

Addition of hydrogen (H2) to gas turbine fuel has recently become a topic of interest facing the global challenges of CO2 free combustion. As a drawback, Nitrogen oxide (NOx) emissions are likely to increase in hydrogen-rich fuel combustion which in return limits the use of the technology. In the course of this development, a model-based quantification of NOx emission increase by fuel flexibility may identify possible operation ranges of this technology. This paper evaluates the effect of an increased hydrogen fraction in the fuel on the NOx emissions of a non-premixed 10 MWth gas turbine combustor. A simple reactor network model has been set up using a perfectly stirred reactor (PSR) to simulate the flame zone and a plug flow reactor (PFR) to simulate the post flame zone. The change of residence time in the flame zone is accounted for by an empirical expression. The model is validated against data from high-pressure test rig experiments of an industrial non-premixed gas turbine combustor. The model results are in good agreement with the experimental data. Based on the model results, a fundamental correlation of the effect of hydrogen on the NOx emissions is formulated.

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 were 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 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.

Emission measurements are a valuable source of information regarding the condition of gas turbine combustors. Aging of the hot gas path components can lead to an emission increase, which may ultimately require a readjustment of operational settings and accordingly impacts plant availability and maintenance. While NOx emissions may become crucial in high flame temperatures at full load, carbon monoxide emissions typically restrict low-load operation, which electricity markets demand more frequently due to the increasing penetration of intermittent renewable power. This paper presents a semiempirical carbon monoxide model that allows for quantifying the evolution of carbon monoxide emissions for GT24/GT26 heavy-duty gas turbines in commercial long-term operation. Input parameters to the derived carbon monoxide model are either directly measured or reconstructed by virtual measurements based on a simplified engine model. The method is developed with commissioning and operation data of three different gas turbines of GE’s GT24/GT26 fleet and validated over a total of 8.5 years of observation. Aging is accounted for by incorporating control sensor deviation and the formation of cold spots in the combustor into the semiempirical model. When these effects are taken into account, the carbon monoxide prediction is improved by up to 60% in terms of root mean square error of the log10(carbon monoxide) values compared to a benchmark case without consideration of aging.

Within the ongoing global transition process towards renewable energies, gas turbines can play a significant role due to their ability to provide flexible and dispatchable power that compensates for the inherent volatility of renewable power generation. While being important for the stability of the electricity grid, this flexible mode of operation may result in a significant increase in thermo-mechanical stress for the gas turbine components. The demand for a constantly high level of performance and availability despite these challenges requires the employment of comprehensive monitoring tools. While different monitoring tools may vary in detail, a common core element is the inherent model capability to predict the ideal operational characteristics of the gas turbine for varying boundary conditions. In literature, various approaches are suggested to generate these gas turbine models. Within the present study, the authors apply a data-driven and a physically based modeling approach to two real long-term monitoring scenarios and compare different evaluation metrics. The overall goal of the study is the identification of advantages and disadvantages of the investigated modeling approaches depending on the monitoring scenario. The first part of the study takes the perspective of a gas turbine operator mainly focusing on the monitoring of the thermodynamic performance parameters. This perspective is characterized by the availability of a comprehensive set of long-term operational data on the one hand but the lack of detailed design information regarding the component characteristics of the operated gas turbine on the other hand. In the present study, the operational data set is provided by an E-class gas turbine that is operated in a Chinese combined cycle power plant. The physically based modelling approach used for this scenario is mainly based on a combination of heat- and mass balances representing a simplified thermodynamic gas turbine process. In addition, publicly available component maps are modified and subsequently integrated into the model. The corresponding data based modelling approach utilizes the set of long-term operational data as input parameters for the development of an artificial multi-layer perceptron neural network model with one hidden layer. The development steps conducted within the present study include the selection of adequate input and output parameters, the pre-processing of the data set for training and a sensitivity analysis regarding the number of neurons in the hidden layer. In summary, the results show that the data based model approach outperforms the physically based model approach based on an evaluation of the RMSE and the nRMSE. However, both the data based model approach and the physically based model approach are able to capture the main operational characteristics of the investigated gas turbine within the complete load range making both approaches suitable approach for long-term monitoring scenarios.