Research Papers: Gas Turbines: Cycle Innovations

Bayesian Calibration for Power Splitting in Single-Shaft Combined Cycle Plant Diagnostics

[+] Author and Article Information
Xiaomo Jiang

General Electric Company,
Power & Water, Fleet Management,
Atlanta, GA 30339
e-mail: xiaomo.jiang@ge.com

TsungPo Lin

General Electric Company,
Power & Water, Life Cycle Engineering,
Atlanta, GA 30339
e-mail: tsungpo.lin@ge.com

Eduardo Mendoza

General Electric Company,
Power & Water, Life Cycle Engineering,
Atlanta, GA 30339
e-mail: eduardo.mendoza@ge.com

1Corresponding author.

Contributed by the Cycle Innovations Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received July 13, 2015; final manuscript received August 20, 2015; published online November 3, 2015. Editor: David Wisler.

J. Eng. Gas Turbines Power 138(5), 051702 (Nov 03, 2015) (9 pages) Paper No: GTP-15-1268; doi: 10.1115/1.4031564 History: Received July 13, 2015; Revised August 20, 2015

Condition monitoring and diagnostics of a combined cycle gas turbine (CCGT) power plant has become an important tool to improve its availability, reliability, and performance. However, there are two major challenges in the diagnostics of performance degradation and anomaly in a single-shaft combined cycle (CC) power plant. First, since the gas turbine (GT) and steam turbine (ST) in such a plant share a common generator, each turbine's contribution to the total plant power output is not directly measured, but must be accurately estimated to identify the possible causes of plant level degradation. Second, multivariate operational data instrumented from a power plant need to be used in the plant model calibration, power splitting, and degradation diagnostics. Sensor data always contain some degree of uncertainty. This adds to the difficulty of both estimation of GT to ST power split (PS) and degradation diagnostics. This paper presents an integrated probabilistic methodology for accurate power splitting and the degradation diagnostics of a single-shaft CC plant, accounting for uncertainties in the measured data. The method integrates the Bayesian inference approach, thermodynamic physics modeling, and sensed operational data seamlessly. The physics-based thermodynamic heat balance model is first established to model the power plant components and their thermodynamic relationships. The model is calibrated to model the plant performance at the design conditions of its main components. The calibrated model is then employed to simulate the plant performance at various operating conditions. A Bayesian inference method is next developed to determine the PS between the GT and the ST by comparing the measured and expected power outputs at different operation conditions, considering uncertainties in multiple measured variables. The calibrated model and calculated PS are further applied to pinpoint the possible causes at individual components resulting in the plant level degradation. The proposed methodology is demonstrated using operational data from a real-world single-shaft CC power plant with a known degradation issue. This study provides an effective probabilistic methodology to accurately split the power for degradation diagnostics of a single-shaft CC plant, addressing the uncertainties in multiple measured variables.

Copyright © 2016 by ASME
Your Session has timed out. Please sign back in to continue.


Chase, D. L. , and Kehoe, P. T. , 2000, Combined-Cycle Product Line and Performance, GER-3574G, GE Power Systems, Schenectady, NY.
Kehlhofer, R. , Rukes, B. , Hannemann, F. , and Sturbunabbm, F. , 2009, Combined-Cycle Gas Steam Turbine Power Plants, PennWell, Tulsa, OK.
ASME, 2010, Performance Monitoring Guidelines for Power Plants—Performance Test Codes, American Society of Mechanical Engineers, New York.
Balevic, D. , Hartman, S. , and Youmans, R. , 2010, Heavy-Duty Gas Turbine Operating and Maintenance Considerations, GER-3620L.1, GE Energy, Atlanta, GA.
Jiang, X. , and Foster, C. , 2013, “ Remote Thermal Performance Monitoring and Diagnostics—Turning Data Into Knowledge,” ASME Paper No. POWER2013-98246.
Jiang, X. , and Foster, C. , 2014, “ Plant Performance Monitoring and Diagnostics—Remote, Real-Time and Automation,” ASME Paper No. GT2014-27314.
Meher-Homji, C. B. , Chaker, M. A. , and Motiwala, H. M. , 2001, “ Gas Turbine Performance Deterioration,” 30th Turbomachinery Symposium, Houston, TX, Sept. 17–20, pp. 139–175.
Trucano, T. G. , Swiler, L. P. , Igusa, T. , Oberkampf, W. L. , and Pilch, M. , 2006, “ Calibration, Validation, and Sensitivity Analysis: What's What,” Reliab. Eng. Syst. Saf., 91(10–11), pp. 1331–1357. [CrossRef]
Hill, M. C. , 1998, “ Methods and Guidelines for Effective Model Calibration,” USGS Water-Resources Investigations Report, Boulder, CO, Report No. 98-4005.
Higdon, D. , Gattiker, J. , Williams, B. , and Rightley, M. , 2008, “ Computer Model Calibration Using High-Dimensional Output,” J. Am. Stat. Assoc., 103(482), pp. 570–583. [CrossRef]
Fu, Y. , Jiang, X. , and Yang, R.-J. , 2009, “ Auto-Correlation of Occupant Restraint System Model Using Bayesian Model Validation Metric,” SAE Technical Paper No. 2009-01-1402.
Jiang, X. , and Mahadevan, S. , 2009, “ Bayesian Inference Method for Model Validation and Confidence Extrapolation,” J. Appl. Stat., 36(6), pp. 659–677. [CrossRef]
Jiang, X. , Yang, R.-J. , Barbat, S. , and Weerappuli, P. , 2009, “ Bayesian Probabilistic PCA Approach for Model Validation of Dynamic Systems,” SAE Int. J. Mater. Manuf., 2(1), pp. 555–563. [CrossRef]
Kennedy, M. C. , and O'Hagan, A. , 2001, “ Bayesian Calibration of Computer Experiments,” J. R. Stat. Soc. Ser. B, 63(3), pp. 425–464. [CrossRef]
Higdon, D. , Kennedy, M. , Cavendish, J. C. , Cafeo, J. , and Ryne, R. D. , 2006, “ Combining Field Data and Computer Simulations for Calibration and Prediction,” SIAM J. Sci. Comput., 26(2), pp. 448–466. [CrossRef]
Higdon, D. , Nakhleh, C. , Gattiker, J. , and Williams, B. , 2008, “ A Bayesian Calibration Approach to the Thermal Problem,” Comput. Methods Appl. Mech. Eng. (special issue), 197(29–32), pp. 2431–2441. [CrossRef]
Tagade, P. M. , Sudhakar, K. , and Sane, S. K. , 2009, “ Bayesian Framework for Calibration of Gas Turbine Simulator,” J. Propul. Power, 25(4), pp. 987–992. [CrossRef]
Yuan, J. , and Ng, S. H. , 2013, “ A Sequential Approach for Stochastic Computer Model Calibration and Prediction,” Reliab. Eng. Syst. Saf., 111(3), pp. 273–286. [CrossRef]
Li, P. , Flynn, D. , and Cregan, M. , 2007, “ Statistical Model for Power Plant Performance Monitoring and Analysis,” 42nd International Universities Power Engineering Conference (UPEC), Sept. 4–6, Brighton, UK, pp. 121–126.
Świrski, K. , 2011, “ Power Plant Performance Monitoring Using Statistical Methodology Approach,” J. Power Technol., 91(2), pp. 63–76.
Jensen, F. V. , and Jensen, F. B. , 2001, Bayesian Networks and Decision Graphs, Springer-Verlag, New York.
Gilks, G. R. , Richardson, S. , and Spiegelhalter, D. J. , 1996, “ Markov Chain Monte Carlo in Practice,” Interdisciplinary Statistics, Chapman & Hall/CRC, London.
Spiegelhalter, D. J. , Thomas, A. , and Best, N. G. , 2002, winbugs Version 1.4 User Manual, MRC Biostatistics Unit, Cambridge, UK.


Grahic Jump Location
Fig. 1

Example of corrected power output, degradation, and performance recovery after offline WW

Grahic Jump Location
Fig. 2

Concept of parameter calibration

Grahic Jump Location
Fig. 3

Data flow for performance calculation in a CCGT plant

Grahic Jump Location
Fig. 4

Graphical representation of a single-shaft CC plant

Grahic Jump Location
Fig. 5

Model-based heat balance calculation at component level

Grahic Jump Location
Fig. 6

Procedure of power splitting

Grahic Jump Location
Fig. 7

Concept of Bayes theorem

Grahic Jump Location
Fig. 8

Thermodynamic plant model

Grahic Jump Location
Fig. 9

Histogram of PS results

Grahic Jump Location
Fig. 10

Plant performance degradation breakdown using physics model and PS



Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In