Research Papers: Gas Turbines: Controls, Diagnostics, and Instrumentation

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 52074, Germany
e-mail: moritz.lipperheide@rwth-aachen.de

Frank Weidner, Manfred Wirsum

Institute for Power Plant Technology,
Steam and Gas Turbines,
Department of Mechanical Engineering,
RWTH Aachen University,
Aachen 52074, Germany

Martin Gassner, Stefano Bernero

GE Power,
Baden 5401, Switzerland

1Corresponding author.

2Present address: Alpiq Suisse SA, Lausanne 1003, Switzerland.

Contributed by the Controls, Diagnostics and Instrumentation Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received March 13, 2018; final manuscript received April 12, 2018; published online June 19, 2018. Editor: David Wisler.

J. Eng. Gas Turbines Power 140(10), 101601 (Jun 19, 2018) (10 pages) Paper No: GTP-18-1127; doi: 10.1115/1.4040009 History: Received March 13, 2018; Revised April 12, 2018

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.

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Fig. 1

GT24/GT26 components and features

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Fig. 2

Simplified model with real and virtual measurements for description of engine behavior

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Fig. 3

NOx measurements over calculated flame temperature and corresponding fit with expected e-function shape for ≈ constant pressure. All scales are normalized to a reference value.

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Fig. 5

Aging induced decrease of combustor discharge coefficient for each engine. Redundant measurements (#1 and #2) for engine A are separately shown for single sensor fault detection.

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Fig. 6

Environmental burner geometry and correspondent flow network model with expected flow coefficient trend

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Fig. 4

Difference between turbine outlet temperature measurements versus operation time (smoothed shape and offset for engine B/C due to infrequent data)

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Fig. 8

Long-term NOx prediction with applied aging phenomena (a) engine A, (b) engine B, and (c) engine C (all scales normalized)

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Fig. 9

Residuals for case 1 and 4 for (a) engine A, (b) engine B, and (c) engine C (all scales normalized)

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Fig. 10

NOx emissions measured by CEMS versus reference emission measurement during commissioning (all scales normalized)

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Fig. 7

Commissioning NOx prediction (a) engine A, (b) engine B, and (c) engine C (all scales normalized)



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