Implementation of an adaptive thermodynamic fault model to compensate the gas turbine degradation

Document Type : Original Article

Authors

1 PhD student, K. N. Toosi University of Technology, Tehran, Iran

2 Professor , K. N. Toosi University of Technology, Tehran, Iran

Abstract

In this research, a thermodynamic model has been developed to simulate the effects of fouling and erosion of the compressor blades of a V94.2 gas turbine. The novel approach of this study involves considering the influence of ambient temperature and humidity on the performance of a faulty turbine as well as using the turbine control system under full load conditions, referred to as Outlet Temperature Control (OTC). In this approach, maintaining a corrected turbine outlet temperature is employed to keep the turbine inlet temperature within a safe range for the blades. This model has been validated using real-world data of a gas turbine. The results demonstrate that by adjusting the OTC control setpoint and taking into account the turbine inlet temperature, a portion of the performance losses can be compensated for. The findings indicate that compressor fouling has a greater impact on parameters such as power output, turbine inlet temperature, and gas turbine efficiency compared to blade erosion. Furthermore, deviation from healthy performance varies with environmental conditions. The results also show that by increasing the control setpoint of a degraded turbine by 6 degrees, considering ambient temperature, power can be increased by 1%, and turbine inlet temperature can be increased by 0.8%.

Keywords

Smiley face

References

[1] Salilew WM, Abdul Karim ZA, Lemma TA, Fentaye AD, Kyprianidis KG. The Effect of Physical Faults on a Three-Shaft Gas Turbine Performance at Full-and Part-Load Operation. Sensors. 2022;22(19):7150.
[2] Zwebek A, Pilidis P. Degradation effects on combined cycle power plant performance—part I: gas turbine cycle component degradation effects. J Eng Gas Turbines Power. 2003;125(3):651-7.
[3] Gobran M. Off-design performance of solar Centaur-40 gas turbine engine using Simulink. Ain Shams Engineering Journal. 2013;4(2):285-98.
[4] Kurz R, Brun K. Degradation in gas turbine systems. J Eng Gas Turbines Power. 2001;123(1):70-7.
[5] Mohammadi E, Montazeri-Gh M. Simulation of full and part-load performance deterioration of industrial two-shaft gas turbine. Journal of Engineering for Gas Turbines and Power. 2014;136(9):092602.
[6] Ogaji S, Sampath S, Singh R, Probert S. Parameter selection for diagnosing a gas-turbine's performance-deterioration. Applied energy. 2002;73(1):25-46.
[7] Razak A. Industrial gas turbines: performance and operability: Elsevier; 2007.
[8] Evstifeev A, Kazarinov N, Petrov Y, Witek L, Bednarz A. Experimental and theoretical analysis of solid particle erosion of a steel compressor blade based on incubation time concept. Engineering Failure Analysis. 2018;87:15-21.
[9] Kurz R, Brun K. Degradation of gas turbine performance in natural gas service. Journal of Natural Gas Science and Engineering. 2009;1(3):95-102.
[10] Meher-Homji C, Bromley A, Stalder J-P, editors. Gas turbine performance deterioration and compressor washing. Middle East Turbomachinery Symposia 2013 Proceedings; 2013: Turbomachinery Laboratory, Texas A&M Engineering Experiment Station.
[11] Wilcox M, Baldwin R, Garcia-Hernandez A, Brun K. Guideline for gas turbine inlet air filtration systems. Gas Machinery Research Council, Dallas, TX. 2010.
[12] Diakunchak IS. Performance deterioration in industrial gas turbines. Journal of Engineering for Gas Turbines and Power;(United States). 1992;114(2).
[13] Igie U, Diez-Gonzalez P, Giraud A, Minervino O. Evaluating gas turbine performance using machine-generated data: quantifying degradation and impacts of compressor washing. Journal of Engineering for Gas Turbines and Power. 2016;138(12):122601.
[14] Meher-Homji CB, Bromley A, editors. Gas Turbine Axial Compressor Fouling And Washing. Proceedings of the 33rd turbomachinery symposium; 2004: Texas A&M University. Turbomachinery Laboratories.
[15] Aretakis N, Roumeliotis I, Doumouras G, Mathioudakis K. Compressor washing economic analysis and optimization for power generation. Applied energy. 2012;95:77-86.
[16] Doel D. TEMPER—a gas-path analysis tool for commercial jet engines. 1994.
[17] Urban LA. Gas path analysis applied to turbine engine condition monitoring. Journal of Aircraft. 1973;10(7):400-6.
[18] Chen Y-Z, Zhao X-D, Xiang H-C, Tsoutsanis E. A sequential model-based approach for gas turbine performance diagnostics. Energy. 2021;220:119657.
[19] Li J, Ying Y. Gas turbine gas path diagnosis under transient operating conditions: A steady state performance model based local optimization approach. Applied Thermal Engineering. 2020;170:115025.
 [20] Ying Y, Li J. An improved performance diagnostic method for industrial gas turbines with consideration of intake and exhaust system. Applied Thermal Engineering. 2023;222:119907.
[21] Zhang Y, Liu P, Li Z. Gas turbine off-design behavior modelling and operation windows analysis under different ambient conditions. Energy. 2023;262:125348.
[22] Nekoonam, A, Montazeri M.Thermodynamic Simulation of Fouling and Erosion in an Industrial Gas Turbine for Power Generation Applications;74(4):53-69. 2023
 [23] Volponi AJ, DePold H, Ganguli R, Daguang C. The use of Kalman filter and neural network methodologies in gas turbine performance diagnostics: a comparative study. J Eng Gas Turbines Power. 2003;125(4):917-24.
[24] Alblawi A. Fault diagnosis of an industrial gas turbine based on the thermodynamic model coupled with a multi feedforward artificial neural networks. Energy Reports. 2020;6:1083-96.
[25] Hanachi H, Liu J, Kim IY, Mechefske CK. Hybrid sequential fault estimation for multi-mode diagnosis of gas turbine engines. Mechanical systems and signal processing. 2019;115:255-68.
[26] Talaat M, Gobran M, Wasfi M. A hybrid model of an artificial neural network with thermodynamic model for system diagnosis of electrical power plant gas turbine. Engineering Applications of Artificial Intelligence. 2018;68:222-35.
[27] Zhong S-s, Fu S, Lin L. A novel gas turbine fault diagnosis method based on transfer learning with CNN. Measurement. 2019;137:435-53.
[28] Zhou D, Wei T, Huang D, Li Y, Zhang H. A gas path fault diagnostic model of gas turbines based on changes of blade profiles. Engineering Failure Analysis. 2020;109:104377.
[29] Cloyd ST, Harris Jr AJ. Gas turbine performance: new application and test correction curves: American Society of Mechanical Engineers; 1995.
[30] Walsh PP, Fletcher P. Gas turbine performance: John Wiley & Sons; 2004.
[31] Kim TS. Model-based performance diagnostics of heavy-duty gas turbines using compressor map adaptation. Applied energy. 2018;212:1345-59.
[32] Standardization I. ISO 2314 Gas turbines--Acceptance tests. I; 2009.
[33] Rashidzadeh H, Hosseinalipour SM, Mohammadzadeh A. The SGT-600 industrial twin-shaft gas turbine modeling for mechanical drive applications at the steady state conditions. Journal of Mechanical Science and Technology. 2015;29:4473-81.
[34] Sanaye S, Hosseini S. Prediction of blade life cycle for an industrial gas turbine at off-design conditions by applying thermodynamics, turbo-machinery and artificial neural network models. Energy Reports. 2020;6:1268-85.
[35] Montazeri-Gh M, Nekoonam A. Gas path component fault diagnosis of an industrial gas turbine under different load condition using online sequential extreme learning machine. Engineering Failure Analysis. 2022;135:106115.
Fluid Mechanics & Aerodynamics
Volume 12, Issue 1 - Serial Number 31
October 2023
Pages 107-121

Files

History

  • Receive Date: 28 March 2023
  • Revise Date: 14 July 2023
  • Accept Date: 01 August 2023
  • Publish Date: 25 August 2023

Share

How to cite

Statistics