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

Gas Turbine Modeling for Diagnosis and Control

[+] Author and Article Information
Emil Larsson

Division of Vehicular Systems,
Department of Electrical Engineering,
Linköping University,
Linköping SE-581 83, Sweden
e-mail: lime@isy.liu.se

Jan Åslund

Division of Vehicular Systems,
Department of Electrical Engineering,
Linköping University,
Linköping SE-581 83, Sweden
e-mail: jaasl@isy.liu.se

Erik Frisk

Division of Vehicular Systems,
Department of Electrical Engineering,
Linköping University,
Linköping SE-581 83, Sweden
e-mail: frisk@isy.liu.se

Lars Eriksson

Division of Vehicular Systems,
Department of Electrical Engineering,
Linköping University,
Linköping SE-581 83, Sweden
e-mail: larer@isy.liu.se

1Address all correspondence to this author.

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 22, 2013; final manuscript received January 14, 2014; published online February 18, 2014. Assoc. Editor: Allan Volponi.

J. Eng. Gas Turbines Power 136(7), 071601 (Feb 18, 2014) (17 pages) Paper No: GTP-13-1084; doi: 10.1115/1.4026598 History: Received March 22, 2013; Revised January 14, 2014

The supervision of performance in gas turbine applications is crucial in order to achieve: (i) reliable operations, (ii) low heat stress in components, (iii) low fuel consumption, and (iv) efficient overhaul and maintenance. To obtain a good diagnosis of performance it is important to have tests which are based on models with high accuracy. A main contribution is a systematic design procedure to construct a fault detection and isolation (FDI) system for complex nonlinear models. To fulfill the requirement of an automated design procedure, a thermodynamic gas turbine package (GTLib) is developed. Using the GTLib framework, a gas turbine diagnosis model is constructed where component deterioration is introduced. In the design of the test quantities, equations from the developed diagnosis model are carefully selected. These equations are then used to implement a constant gain extended Kalman filter (CGEKF)-based test quantity. The test quantity is used in the FDI-system to supervise the performance and in the controller to estimate the flame temperature. An evaluation is performed using experimental data from a gas turbine site. The case study shows that the designed FDI-system can be used when the decision about a compressor wash is taken. Thus, the proposed model-based design procedure can be considered when an FDI-system of an industrial gas turbine is constructed.

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Volponi, A. J., 2014, “Gas Turbine Engine Health Management: Past, Present, and Future Trends,” ASME J. Eng. Gas Turbines Power, 136(5), p. 051201. [CrossRef]
Luppold, R. H., Roman, J. R., Gallops, G. W., and Kerr, L. J., 1989, “Estimating In-Flight Engine Performance Variations Using Kalman Filter Concepts,” AIAA/ASME/SAE/ASEE 25th Joint Propulsion Conference, Monterey, CA, July 10–12, AIAA Paper No. 89-2584. [CrossRef]
Kobayashi, T. and Simon, D. L., 2005, “Evaluation of an Enhanced Bank of Kalman Filters for In-Flight Aircraft Engine Sensor Fault Diagnostics,” ASME J. Eng. Gas Turbines Power, 127(3), pp. 497–504. [CrossRef]
Watts, J. W., Dwan, T. E., and Brockus, C. G., 1992, “Optimal State-Space Control of a Gas Turbine Engine,” ASME J. Eng.Gas Turbines Power, 114(4), pp. 763–767. [CrossRef]
Härefors, M., 1997, “Application of h Robust Control to the RM12 Jet Engine,” Control Eng. Pract., 5(9), pp. 1189–1201. [CrossRef]
Borguet, S. and Léonard, O., 2008, “A Sensor-Fault-Tolerant Diagnosis Tool Based on a Quadratic Programming Approach,” ASME J. Eng. Gas Turbines Power, 130(2), p. 021605. [CrossRef]
Stephanopoulos, G. N., Aristidou, A. A., and Nielsen, J., 1998, Metabolic Engineering: Principles and Methodologies, 1st ed., Academic, New York.
Gordon, S. and McBride, B. J., 1994, Computer Program for Calculation of Complex Chemical Equilibrium Compositions and Applications I. Analysis,” National Aeronautics and Space Administration (NASA), Washington, DC, Technical Report No. RP-1311.
Heywood, J. B., 1988, Internal Combustion Engine Fundamentals (McGraw-Hill Series in Mechanical Engineering), McGraw-Hill, New York.
Buck, A. L., 1981, “New Equations for Computing Vapor Pressure and Enhancement Factor,” J. Appl. Meterol., 20(12), p. 1529. [CrossRef]
Larsson, E., 2012, “Diagnosis and Supervision of Industrial Gas Turbines,” Ph.D. thesis, Department of Electrical Engineering, Linköping University. Linköping, Sweden, Thesis No. LiU-TEK-LIC-2012:13, http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-75985
Saravanamuttoo, H., Rogers, G., and Cohen, H., 2001, Gas Turbine Theory, 5th ed., Longman, New York.
Volponi, A. J., 1999, “Gas Turbine Parameter Corrections,” ASME J. Eng. Gas Turbines Power, 121(4), pp. 613–621. [CrossRef]
Dixon, S. and Hall, C., 2010, Fluid Mechanics and Thermodynamics of Turbomachinery, 6th ed., Elsevier, New York.
Idebrant, A. and Näs, L., 2003, “Gas Turbine Applications Using Thermofluid,” 3rd International Modelica Conference, Linköping, Sweden, November 3–4, P. Fritzson, ed., The Modelica Association, Linköping, Sweden, pp. 359–366.
Larsson, E., Åslund, J., Frisk, E., and Eriksson, L., 2011, “Health Monitoring in an Industrial Gas Turbine Application by Using Model Based Diagnosis Techniques,” ASME Paper No. GT2011-46825. [CrossRef]
Dassault Systemes, 2012, “Dynamic Modeling Laboratory (Dymola) 7.2.”
Kobayashi, T. and Simon, D. L., 2003, “Application of a Bank of Kalman Filters for Aircraft Engine Fault Diagnostics,” NASA Report No. NASA/TM-2003-212526.
Hairer, E., Norsett, S. P., and Wanner, G., 1991, Solving Ordinary Differential Equations II: Stiff and Differential-Algebraic Problems, 2nd ed. rev., Springer, Berlin.
Blanke, M., Kinnaert, M., Lunze, J., and Staroswiecki, M., 2003, Diagnosis and Fault-Tolerant Control, Springer, New York.
Ascher, U. M. and Petzold, L. R., 1998, Computer Methods for Ordinary Differential Equations and Differential-Algebraic Equations, 1st ed., Society for Industrial and Applied Mathematics (SIAM), Philadelphia, PA.
Mattsson, S. and Söderlind, G., 1993, “Index Reduction in Differential-Algebraic Equations Using Dummy Derivatives,” SIAM J. Sci. Comput., 14(3), pp. 677–692. [CrossRef]
Pantelides, C. C., 1988, “The Consistent Initialization of Differential-Algebraic Systems,” SIAM J. Sci. Stat. Comput., 9(2), pp. 213–231. [CrossRef]
Dulmage, A. L. and Mendelsohn, N. S., 1958, “Coverings of Bipartite Graphs,” Can. J. Math., 10, pp. 517–534. [CrossRef]
Hermann, R. and Krener, A., 1977, “Nonlinear Controllability and Observability,” IEEE Trans. Autom. Control, 22(5), pp. 728–740. [CrossRef]
Nijmeijer, H. and Fossen, T., 1999, New Directions in Nonlinear Observer Design (Lecture Notes in Control and Information Sciences), Springer, New York.
Shields, R. and Pearson, J., 1976, “Structural Controllability of Multi-Input Linear Systems,” IEEE Trans. Autom. Control, 21(2), pp. 203–212. [CrossRef]
Urban, L. A., and Volponi, A. J., 1992, “Mathematical Methods of Relative Engine Performance Diagnostics,” SAE Technical Paper No. 922048. [CrossRef]
Dewallef, P., Romessis, C., Léonard, O., and Mathioudakis, K., 2006, “Combining Classification Techniques With Kalman Filters for Aircraft Engine Diagnostics,” ASME J. Eng. Gas Turbines Power, 128(2), pp. 281–287. [CrossRef]
Urban, L. A., and Volponi, A. J., 1992, “Mathematical Methods of Relative Engine Performance Diagnostics,” SAE Technical Paper No. 922048. [CrossRef]
Safonov, M. and Athans, M., 1978, “Robustness and Computational Aspects of Nonlinear Stochastic Estimators and Regulators,” IEEE Trans. Autom. Control, 23(4), pp. 717–725. [CrossRef]
Kailath, T., Sayed, A. H., and Hassibi, B., 2000, Linear Estimation, 2nd ed., Prentice-Hall, Upper Saddle River, NJ.
Kobayashi, T., Simon, D. L., and Litt, J. S., 2005, “Application of a Constant Gain Extended Kalman Filter for In-Flight Estimation of Aircraft Engine Performance Parameters,” NASA Report No. NASA/TM-2005-213865.
Sugiyama, N., 2000, “System Identification of Jet Engines,” ASME J. Eng. Gas Turbines Power, 122(1), pp. 19–26. [CrossRef]
Scott, J., 1986, “Reducing Turbo Machinery Operating Costs With Regular Performance Testing,” ASME International Gas Turbine Conference and Exhibit, Dusseldorf, Germany, June 8–12, ASME Paper No. 86-GT-173.


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Fig. 1

Overview of the experimental and simulation platform together with the key contribution of the systematic design procedure. The FDI-system design consists of the three main steps: (a) gas turbine modeling, (b) diagnosis modeling, and (c) test quantity generation. The goal with the design is to have the same performance and accuracy in the FDI-system as in the gas turbine model used in the simulation platform simultaneously, since the design procedure is simple enough.

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Fig. 2

Structure of the gas model

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Fig. 3

Conservation of mass and energy

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Fig. 4

Typical appearance of the performance characteristics together with the surge line and the choke line of the compressor

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Fig. 5

Typical appearance of the performance characteristic of a power turbine for different normalized speeds

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Fig. 6

Simulation platform

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Fig. 7

Graphical representation of the diagnosis model

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Fig. 8

The Dulmage–Mendelsohn decomposition of the gas turbine diagnosis model, where the x-axis represents the unknown variables and the y-axis represents the equations. It may be possible to calculate the variables in set X0 using the equation set M0 (if the variables in set X+ are known) since this part of the model is exactly determined and no redundancy is available.

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Fig. 9

The gas turbine with the output signals (solid lines), the input signals (dashed lines), the input ambient signals (dotted lines), and health parameters (arrows) is shown in the figure. The secondary air flows, used to cool the first turbine blades, are shown with dashed arrows.

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Fig. 10

Flame temperature difference ΔTf and residuals ri between the CGEKFs, which does compensate (red line) and does not compensate (blue line) for the performance degradation in the gas turbine

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Fig. 11

Estimation of the health parameter during a time interval of one year. The degradation in the efficiency of C1 can be bounded to an interval of about 2 percentage points, independent of the atmospheric weather conditions during the year. Thus, it is possible to use a threshold Lw to detect when it is time to wash the compressor. The compressor is washed five times: (i) mid-November, (ii) end of December, (iii) end of March, (iv) end of June, and (v) mid-September. When the compressor wash is performed, the efficiency goes up to L0. A degradation in mass flow is shown but it is not as easy to have a static threshold in that case.



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