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Research Papers: Gas Turbines: Turbomachinery

Evaluating Gas Turbine Performance Using Machine-Generated Data: Quantifying Degradation and Impacts of Compressor Washing

[+] Author and Article Information
Uyioghosa Igie

School of Aerospace, Transport and
Manufacturing (SATM),
Cranfield University,
Bedfordshire MK43 0AL, UK
e-mail: u.igie@cranfield.ac.uk

Pablo Diez-Gonzalez, Antoine Giraud, Orlando Minervino

School of Aerospace, Transport and
Manufacturing (SATM),
Cranfield University,
Bedfordshire MK43 0AL, UK

Contributed by the Turbomachinery Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received October 31, 2015; final manuscript received March 28, 2016; published online July 19, 2016. Assoc. Editor: Klaus Brun.

J. Eng. Gas Turbines Power 138(12), 122601 (Jul 19, 2016) (18 pages) Paper No: GTP-15-1508; doi: 10.1115/1.4033748 History: Received October 31, 2015; Revised March 28, 2016

Gas turbine (GT) operators are often met with the challenge of utilizing and making meaning of the vast measurement data collected from machine sensors during operation. This can easily be about 576 × 106 data points of gas path measurements for one machine in a base load operation in a year, if the width of the data is 20 columns of measured and calculated parameters. This study focuses on the utilization of large data in the context of quantifying the degradation that is mostly related to compressor fouling, in addition to investigations on the impact of offline and online compressor washing. To achieve this, four GT engines operating for about 3.5 years with 51 offline washes and 1184 occasions of online washes were examined. This investigation includes different wash frequencies, liquid concentrations, and one engine operation without online washing (only offline). This study has involved correcting measurement data not only just with compressor inlet temperatures (CITs) and pressures but also with relative humidity (RH). turbomatch, an in-house GT performance simulation software has been implemented to obtain nondimensional factors for the corrections. All of the data visualization and analysis have been conducted using tableau analytics software, which facilitates the investigation of global and local events within an operation. The concept of using of handles and filters is proposed in this study, and it demonstrates the level of insight to the data and forms the basis of the outcomes obtained. This work shows that during operation, the engine performance is mostly deteriorating, though to varying degrees. Online washing also showed an influence on this, reducing the average degradation rate each hour by half, when compared to the engine operating only with offline washing. Hourly marginal improvements were also observed with an increased average wash frequency of nine hours and a similar outcome obtained when the washing solution is 2.3 times more concentrated. Clear benefits of offline washes are also presented, alongside the typically obtainable values of increased power output after a wash, also in relation to the number of operating hours before a wash.

Copyright © 2016 by ASME
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References

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Figures

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Fig. 1

Fouled blades of a GT engine compressor

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Fig. 2

Compressor washing plenum mounted installation

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Fig. 3

Elementary framework of GT condition monitoring with cloud-based analytics

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Fig. 4

General electric frame 7 F.05 machine [16]

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Fig. 5

GT1 active power output in year A

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Fig. 6

GT1 CIT variation in year A

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Fig. 7

GT1 EGT as a function of time in year A

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Fig. 8

GT1 active power versus VIGV opening at different CIT (60 MW and above)

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Fig. 9

GT1 records for RH for different periods in year A

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Fig. 10

Correction factors for active power output relating CIT and RH

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Fig. 11

Correction factors for active power output for various CIP

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Fig. 12

GT1 active power output in year A and the corrected power output (extended and conventional)

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Fig. 13

Number of records for VIGV opening for GT1 in year A

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Fig. 14

Corrected fuel flow versus fuel flow

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Fig. 15

Corrected fuel flow versus fuel flow with VIGV filter (80–85% opening)

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Fig. 16

The procedure in evaluating performance degradation

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Fig. 17

Trends in corrected power output with time for GT1 (year A)

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Fig. 18

Trends in corrected power output with time for GT2 (year A)

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Fig. 19

Trends in corrected power output with time for GT3 (year A)

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Fig. 20

Trends in corrected power output with time for GT4 (year A)

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Fig. 21

Trends in corrected fuel flow with time for GT1 (year A)

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Fig. 22

Trends in thermal efficiency with time for GT1 (year A)

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Fig. 23

Before and after offline wash—GT1 first wash

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Fig. 24

Before and after offline wash—GT1 second wash

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Fig. 25

Before and after offline wash—GT1 third wash

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Fig. 26

Before and after offline wash—GT1 fourth wash

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Fig. 27

Before and after offline wash—GT1 fifth wash

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Fig. 28

Increase in corrected power output and thermal efficiency as a function of EOH before an offline wash

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Fig. 29

Record distribution of increases in corrected power output for intervals of 0.5% for all the engines

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Fig. 30

Degradation trends for all the periods to be considered

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Fig. 31

Degradation trends of GT1 and GT2 with different wash frequencies but same liquid mix of 4:1

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Fig. 32

Degradation trends of GT2 involving liquid mix of 4:1 and 9:1

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Fig. 33

Degradation trends of GT4 involving liquid mix of 4:1 and water wash

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Fig. 34

Degradation trends of GT2 with compressor wash and GT3 involving no washing

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