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Journal of Engineering for Gas Turbines and Power | Volume 136 | Issue 7 | research-article
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
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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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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.

+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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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

+Structure of the gas model
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Structure of the gas model
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Fig. 3

Conservation of mass and energy

+Conservation of mass and energy
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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

+Typical appearance of the performance characteristics together with the surge line and the choke line of the compressor
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Typical appearance of the performance characteristics together with the surge line and the choke line of the compressor
Grahic Jump Location
Fig. 5

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

+Typical appearance of the performance characteristic of a power turbine for different normalized speeds
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Typical appearance of the performance characteristic of a power turbine for different normalized speeds
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Fig. 6

Simulation platform

+Simulation platform
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Simulation platform
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Fig. 7

Graphical representation of the diagnosis model

+Graphical representation of the diagnosis model
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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.

+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.
View Large  |  Save Figure  |  Download Slide (.ppt)
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.

+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.
View Large  |  Save Figure  |  Download Slide (.ppt)
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

+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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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
Grahic Jump Location
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.

+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.
View Large  |  Save Figure  |  Download Slide (.ppt)
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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