Abstract

Obtaining accurate components' characteristic maps has great significant for gas-turbine operating optimization and gas-path fault diagnosis. A common approach is to modify the original components' characteristic maps by introducing correction factors, which is known as performance adaptation. Among the existing methods, total average prediction error of measurable parameters (MPTAPE) at specified conditions is used to evaluate the adaptation accuracy. However, when a gas turbine undergoes a field operation, the performance parameters of each component are zonally distributed under the operating conditions. Under such circumstances, randomly selecting a few data points as the error control points (ECPs) for performance adaptation may lead to an inappropriate correction of the characteristic maps, further lowering the prediction accuracy of the simulation model. In this paper, a genetic-algorithm-based improved performance adaptation method is proposed, which provides improvements in two aspects. In one aspect, similarity between the components' predicted performance curves and the performance regression curves is used as the criterion with which to evaluate the adaptation accuracy. In the other aspect, in the process of off-design performance adaptation, the performance parameters at the design point are recalibrated. The improved method has been verified by using rig test data and applied to field data of a GE LM2500+SAC gas turbine. The comparison results show that the improved method can obtain more accurate and stable adaptation results, while the computational load can be significantly reduced.

References

1.
Gu
,
C. W.
,
Wang
,
H.
,
Ji
,
X. X.
, and
Li
,
X. S.
,
2016
, “
Development and Application of a Thermodynamic-Cycle Performance Analysis Method of a Three-Shaft Gas Turbine
,”
Energy
,
112
, pp.
307
321
.10.1016/j.energy.2016.06.094
2.
Wang
,
H.
,
Li
,
X. S.
,
Ren
,
X. D.
,
Gu
,
C. W.
, and
Ji
,
X. X.
,
2017
, “
A Thermodynamic-Cycle Performance Analysis Method and Application on a Three-Shaft Gas Turbine
,”
Appl. Therm. Eng.
,
127
, pp.
465
472
.10.1016/j.applthermaleng.2017.08.061
3.
Igie
,
U.
,
Pilidis
,
P.
,
Fouflias
,
D.
,
Ramsden
,
K.
, and
Laskaridis
,
P.
,
2014
, “
Industrial Gas Turbine Performance: Compressor Fouling and on-Line Washing
,”
ASME J. Turbomach.
,
136
(
10
), p.
101001
.10.1115/1.4027747
4.
Tsoutsanis
,
E.
,
Meskin
,
N.
,
Benammar
,
M.
, and
Khorasani
,
K.
,
2015
, “
Transient Gas Turbine Performance Diagnostics Through Nonlinear Adaptation of Compressor and Turbine Maps
,”
ASME J. Eng. Gas Turbines Power
,
137
(
9
), p.
091201
.10.1115/1.4029710
5.
Cornelius
,
C.
,
Biesinger
,
T.
,
Galpin
,
P.
, and
Braune
,
A.
,
2013
, “
Experimental and Computational Analysis of a Multistage Axial Compressor Including Stall Prediction by Steady and Transient CFD Methods
,”
ASME J. Turbomach.
,
136
(
6
), p.
061013
.10.1115/1.4025583
6.
Al-Busaidi
,
W.
, and
Pilidis
,
P.
,
2016
, “
A New Method for Reliable Performance Prediction of Multi-Stage Industrial Centrifugal Compressors Based on Stage Stacking Technique: Part I—Existing Models Evaluation
,”
Appl. Therm. Eng.
,
98
, pp.
10
28
.10.1016/j.applthermaleng.2015.11.115
7.
Al-Busaidi
,
W.
, and
Pilidis
,
P.
,
2015
, “
A New Method for Reliable Performance Prediction of Multi-Stage Industrial Centrifugal Compressors Based on Stage Stacking Technique: Part II—New Integrated Model Verification
,”
Appl. Therm. Eng.
,
90
, pp.
927
936
.10.1016/j.applthermaleng.2015.07.081
8.
Fuls
,
W. F.
,
2017
, “
Accurate Stage-by-Stage Modelling of Axial Turbines Using an Appropriate Nozzle Analogy With Minimal Geometric Data
,”
Appl. Therm. Eng.
,
116
, pp.
134
146
.10.1016/j.applthermaleng.2017.01.048
9.
Yu
,
Y. H.
,
Chen
,
L. G.
,
Sun
,
F. R.
, and
Wu
,
C.
,
2007
, “
Neural-Network Based Analysis and Prediction of a Compressor's Characteristic Performance Map
,”
Appl. Energy
,
84
(
1
), pp.
48
55
.10.1016/j.apenergy.2006.04.005
10.
Misté
,
G. A.
, and
Benini
,
E.
,
2012
, “
Improvements in Off-Design Aeroengine Performance Predictions Using Analytic Compressor Map Interpolation
,”
Int. J. Turbo Jet Eng.
,
29
(
2
), pp.
69
77
.10.1515/tjj-2012-0012
11.
Gaudet
,
S. R.
, and
Gauthier
,
J. E. D.
,
2007
, “
A Simple Sub-Idle Component Map Extrapolation Method
,”
ASME
Paper No. GT2007-27193.10.1115/GT2007-27193
12.
Zachos
,
P. K.
,
Aslanidou
,
I.
,
Pachidis
,
V.
, and
Singh
,
R.
,
2011
, “
A Sub-Idle Compressor Characteristic Generation Method With Enhanced Physical Background
,”
ASME J. Eng. Gas Turbines Power
,
133
(
8
), p.
081702
.10.1115/1.4002820
13.
Roumeliotis
,
I.
,
Aretakis
,
N.
, and
Alexiou
,
A.
,
2016
, “
Industrial Gas Turbine Health and Performance Assessment With Field Data
,”
ASME J. Eng. Gas Turbines Power
,
139
(
5
), p.
051202
.10.1115/1.4034986
14.
Stamatis
,
A.
,
Mathioudakis
,
K.
, and
Papailiou
,
K. D.
,
1990
, “
Adaptive Simulation of Gas Turbine Performance
,”
ASME J. Eng. Gas Turbines Power
,
112
(
2
), pp.
168
175
.10.1115/1.2906157
15.
Lambiris
,
B.
,
Mathioudakis
,
K.
,
Stamatis
,
A.
, and
Papailiou
,
K.
,
1994
, “
Adaptive Modeling of Jet Engine Performance With Application to Condition Monitoring
,”
J. Propul. Power
,
10
(
6
), pp.
890
896
.10.2514/3.23828
16.
Kong
,
C.
,
Ki
,
J.
, and
Kang
,
M.
,
2003
, “
A New Scaling Method for Component Maps of Gas Turbine Using System Identification
,”
ASME J. Eng. Gas Turbines Power
,
125
(
4
), pp.
979
985
.10.1115/1.1610014
17.
Kong
,
C.
,
Kho
,
S.
, and
Ki
,
J.
,
2006
, “
Component Map Generation of a Gas Turbine Using Genetic Algorithms
,”
ASME J. Eng. Gas Turbines Power
,
128
(
1
), pp.
92
96
.10.1115/1.2032431
18.
Kong
,
C.
, and
Ki
,
J.
,
2007
, “
Components Map Generation of Gas Turbine Engine Using Genetic Algorithms and Engine Performance Deck Data
,”
ASME J. Eng. Gas Turbines Power
,
129
(
2
), pp.
312
317
.10.1115/1.2436561
19.
Lo Gatto
,
E.
,
Li
,
Y. G.
, and
Pilidis
,
P.
,
2006
, “
Gas Turbine Off-Design Performance Adaptation Using Genetic Algorithm
,”
ASME
Paper No. GT2006-90299.10.1115/GT2006-90299
20.
Li
,
Y.-G.
,
Marinai
,
L.
,
Pachidis
,
V.
,
Gatto
,
E. L.
, and
Philidis
,
P.
,
2009
, “
Multiple-Point Adaptive Performance Simulation Tuned to Aeroengine Test-Bed Data
,”
J. Propul. Power
,
25
(
3
), pp.
635
641
.10.2514/1.38823
21.
Li
,
Y. G.
,
Abdul Ghafir
,
M. F.
,
Wang
,
L.
,
Singh
,
R.
,
Huang
,
K.
, and
Feng
,
X.
,
2011
, “
Nonlinear Multiple Points Gas Turbine Off-Design Performance Adaptation Using a Genetic Algorithm
,”
ASME J. Eng. Gas Turbines Power
,
133
(
7
), p.
071701
.10.1115/1.4002620
22.
Li
,
Y. G.
,
Abdul Ghafir
,
M. F.
,
Wang
,
L.
,
Singh
,
R.
,
Huang
,
K.
,
Feng
,
X.
, and
Zhang
,
W.
,
2012
, “
Improved Multiple Point Nonlinear Genetic Algorithm Based Performance Adaptation Using Least Square Method
,”
ASME J. Eng. Gas Turbines Power
,
134
(
3
), p.
031701
.10.1115/1.4004395
23.
Tsoutsanis
,
E.
,
Meskin
,
N.
,
Benammar
,
M.
, and
Khorasani
,
K.
,
2014
, “
A Component Map Tuning Method for Performance Prediction and Diagnostics of Gas Turbine Compressors
,”
Appl. Energy
,
135
, pp.
572
585
.10.1016/j.apenergy.2014.08.115
24.
Alberto Misté
,
G.
, and
Benini
,
E.
,
2014
, “
Turbojet Engine Performance Tuning With a New Map Adaptation Concept
,”
ASME J. Eng. Gas Turbines Power
,
136
(
7
), p.
071202
.10.1115/1.4026548
25.
Yang
,
Q. C.
,
Li
,
S. Y.
, and
Cao
,
Y. P.
,
2017
, “
A New Component Map Generation Method for Gas Turbine Adaptation Performance Simulation
,”
J. Mech. Sci. Technol.
,
31
(
4
), pp.
1947
1957
.10.1007/s12206-017-0344-5
26.
Kurz
,
R.
,
Brun
,
K.
, and
Wollie
,
M.
,
2009
, “
Degradation Effects on Industrial Gas Turbines
,”
ASME J. Eng. Gas Turbines Power
,
131
(
6
), p.
062401
.10.1115/1.3097135
27.
Talebi
,
S. S.
, and
Tousi
,
A. M.
,
2017
, “
The Effects of Compressor Blade Roughness on the Steady State Performance of Micro-Turbines
,”
Appl. Therm. Eng.
,
115
, pp.
517
527
.10.1016/j.applthermaleng.2016.12.038
28.
Kang
,
D. W.
, and
Kim
,
T. S.
,
2018
, “
Model-Based Performance Diagnostics of Heavy-Duty Gas Turbines Using Compressor Map Adaptation
,”
Appl. Energy
,
212
, pp.
1345
1359
.10.1016/j.apenergy.2017.12.126
29.
Li
,
Y. G.
, and
Pilidis
,
P.
,
2010
, “
GA-Based Design-Point Performance Adaptation and Its Comparison With ICM-Based Approach
,”
Appl. Energy
,
87
(
1
), pp.
340
348
.10.1016/j.apenergy.2009.05.034
30.
Kurzke
,
J.
,
1996
, “
How to Get Component Maps for Aircraft Gas Turbine Performance Calculations
,”
ASME
Paper No. 96-GT-164.10.1115/96-GT-164
31.
Haglind
,
F.
, and
Elmegaard
,
B.
,
2009
, “
Methodologies for Predicting the Part-Load Performance of Aero-Derivative Gas Turbines
,”
Energy
,
34
(
10
), pp.
1484
1492
.10.1016/j.energy.2009.06.042
32.
Zhang
,
N.
, and
Cai
,
R. X.
,
2002
, “
Analytical Solutions and Typical Characteristics of Part-Load Performances of Single Shaft Gas Turbine and Its Cogeneration
,”
Energy Convers. Manage.
,
43
(
9–12
), pp.
1323
1337
.10.1016/S0196-8904(02)00018-3
33.
Kiaee
,
M.
,
Tousi
,
A. M.
, and
Toudefallah
,
M.
,
2015
, “
Performance Adaptation of a 100 kW Microturbine
,”
Appl. Therm. Eng.
,
87
, pp.
234
250
.10.1016/j.applthermaleng.2015.04.075
34.
Saravanamuttoo
,
H. I. H.
,
Rogers
,
G. F. C.
,
Cohen
,
H.
, and
Straznicky
,
P. V.
,
2009
,
Gas Turbine Theory
, 6th ed.,
Pearson Education
,
UK
, pp.
466
477
.
35.
Li
,
S. Y.
,
Li
,
Z. L.
, and
Huang
,
N.
,
2017
, “
Calculation Model Based Design-Point Gas Generator Performance Adaptation Method
,”
ASME
Paper No. FEDSM2017-69180.10.1115/FEDSM2017-69180
36.
Li
,
S. Y.
,
Li
,
Z. L.
, and
Zhao
,
H. B.
,
2019
, “
Research on the Performance Improvement of a Two-Shaft Gas Turbine With a Variable Area Nozzle Power Turbine
,”
Therm. Sci.
, p.
99
.10.2298/TSCI180714099L
37.
MATLAB
,
2018
, “
MATLAB Version 9.4 (R2018a)
,”
The MathWorks
,
Natick, MA
.
38.
Mohammadi
,
E.
, and
Montazeri-Gh
,
M.
,
2014
, “
Simulation of Full and Part-Load Performance Deterioration of Industrial Two-Shaft Gas Turbine
,”
ASME J. Eng. Gas Turbines Power
,
136
(
9
), p.
092602
.10.1115/1.4027187
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