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

Application of a Statistical Methodology for Gas Turbine Degradation Prognostics to Alstom Field Data

[+] Author and Article Information
Mauro Venturini

Dipartimento di Ingegneria,
Università degli Studi di Ferrara,
Via Giuseppe Saragat, 1,
Ferrara 44122, Italy
e-mail: mauro.venturini@unife.it

Dirk Therkorn

ALSTOM (Switzerland) Ltd,
Brown Boveri Strasse 7,
Baden 5401, Switzerland
e-mail: dirk.therkorn@power.alstom.com

Contributed by the International Gas Turbine Institute (IGTI) of ASME for publication in the Journal of Engineering for Gas Turbines and Power. Manuscript received June 27, 2013; final manuscript received June 28, 2013; published online August 21, 2013. Editor: David Wisler.

J. Eng. Gas Turbines Power 135(9), 091603 (Aug 21, 2013) (10 pages) Paper No: GTP-13-1189; doi: 10.1115/1.4024952 History: Received June 27, 2013; Revised June 28, 2013

In this paper, a prognostic methodology is applied to gas turbine field data to assess its capability as a predictive tool for degradation effects. On the basis of the recordings of past behavior, the methodology provides a prediction of future performance, i.e., the probability that degradation effects are at an acceptable level in future operations. The analyses carried out in this paper consider two different parameters (power output and compressor efficiency) of three different Alstom gas turbine power plants (gas turbine type GT13E2, GT24, and GT26). To apply the prognostic methodology, site specific degradation threshold values were defined, to identify the time periods with acceptable degradation (i.e., higher-than-threshold operation) and the time periods where maintenance activities are recommended (i.e., lower-than-threshold operation). This paper compares the actual distribution of the time points until the degradation limit is reached (discrete by nature) to the continuously varying distribution of the time points simulated by the probability density functions obtained through the prognostic methodology. Moreover, the reliability of the methodology prediction is assessed for all the available field data of the three gas turbines and for two values of the threshold. For this analysis, the prognostic methodology is applied by considering different numbers of degradation periods for methodology calibration and the accuracy of the next forecasted trends is compared to the real data. Finally, this paper compares the prognostic methodology prediction to a “purely deterministic” prediction chosen to be the average of the past time points of higher-than-threshold operations. The results show that, in almost all cases, the prognostic methodology allows a better prediction than the “purely deterministic” approach for both power and compressor efficiency degradation. Therefore, the prognostic methodology seems to be a robust and reliable tool to predict gas turbine power plant “probabilistic” degradation.

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References

Figures

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Fig. 1

Prognostic methodology

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Fig. 2

Data trends of nondimensional power and compressor efficiency for GT13E2 (a), GT24 (b), and GT26 (c)

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Fig. 3

Linearization of GT26 nondimensional power data

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Fig. 4

Significant time points and time frames

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Fig. 5

PDF of actual time points above the degradation limit (dashed line) and of the time points obtained through the prognostic methodology (continuous line) for GT26 power output data for high and low threshold

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Fig. 6

(RMSEk,j)HtT values for power (a) and compressor efficiency (b), in the case where data belong to the same history

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Fig. 7

(RMSEk,j)HtT values for power (a) and compressor efficiency (b), in the case where data are split into different histories

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Fig. 8

Absolute deviation (ΔAk,j)HtT for simulated data

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Fig. 9

Absolute deviation (ΔAk,j)HtT for gas turbine power data for GT13E2 (a), GT24 (b), and GT26 (c)

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Fig. 10

Absolute deviation (ΔAk,j)HtT for compressor efficiency data for GT13E2 (a), GT24 (b) and GT26 (c)

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