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Research Papers: Gas Turbines: Aircraft Engine

Industrial Gas Turbine Health and Performance Assessment With Field Data

[+] Author and Article Information
I. Roumeliotis

Associate Professor
Laboratory of Thermal Turbomachines,
National Technical University of Athens,
Athens 15780, Greece
e-mail: jroume@ltt.ntua.gr

N. Aretakis

Assistant Professor
Laboratory of Thermal Turbomachines,
National Technical University of Athens,
Athens 15780, Greece
e-mail: naret@central.ntua.gr

A. Alexiou

Laboratory of Thermal Turbomachines,
National Technical University of Athens,
Athens 15780, Greece
e-mail: a.alexiou@ltt.ntua.gr

1Present address: Section of Naval Architecture and Marine Engineering, Hellenic Naval Academy, Piraeus, Greece.

Contributed by the Aircraft Engine Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received July 25, 2016; final manuscript received August 31, 2016; published online December 21, 2016. Editor: David Wisler.

J. Eng. Gas Turbines Power 139(5), 051202 (Dec 21, 2016) (10 pages) Paper No: GTP-16-1361; doi: 10.1115/1.4034986 History: Received July 25, 2016; Revised August 31, 2016

The paper presents a thorough analysis of the historical data and results acquired over a period of two years through an on-line real-time monitoring system installed at a combined heat and power (CHP) plant. For gas turbine health and performance assessment, a gas path analysis tool based on the adaptive modeling method is integrated into the system. An engine adapted model built through a semi-automated method is part of a procedure which includes a steam/water cycle simulation module and an economic module used for power plant performance and economic assessment. The adaptive modeling diagnostic method allowed for accurate health assessment during base and part load operation identifying and quantifying compressor recoverable deterioration and the root cause of an engine performance shift. Next, the performance and economic assessment procedure was applied for quantifying the economic benefit accrued by implementing daily on-line washing and for evaluating the financial gains if the off-line washings time intervals are optimized based on actual engine performance deterioration rates. The results demonstrate that this approach allows continuous health and performance monitoring at full and part load operation enhancing decision making capabilities and adding to the information that can be acquired through traditional analysis methods based on heat balance and base load correction curves.

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Figures

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Fig. 1

PROOSIS engine schematic diagram

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Fig. 2

Local adaptation procedure

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Fig. 3

GT data used for adaptation and diagnosis

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Fig. 4

Health factors RMS for calibration data (full load and part load)

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Fig. 5

Health factors RMS versus corrected speed

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Fig. 6

Procedure for calculating power output and heat rate deterioration

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Fig. 7

GT1 relative performance parameters

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Fig. 8

GT1 compressor health factors

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Fig. 9

GT1 turbine health factors

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Fig. 10

GT1 Qexh versus IGVs angle

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Fig. 11

GT1 combustor outlet temperature and TITREF versus power output

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Fig. 12

GT1 PR versus W2,cor

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Fig. 13

GT1 relative power output and compressor efficiency

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Fig. 14

GT1 relative corrected power output

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Fig. 15

GT1 compressor health parameters and performance deterioration

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Fig. 16

GTs performance deterioration for February 2013 and 2014

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Fig. 17

GTs performance deterioration for August 2013 and 2014

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Fig. 18

Power plant performance gain due to on-line washing routine

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Fig. 19

Power profile for the three washing strategies (reference, equidistant, and optimized intervals)

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Fig. 20

GTs deterioration rate during 2014

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