Monitoring the gas turbine start-up phase on a platform using a hierarchical model based on multi-layer perceptron networks

• Autorzy: Niksa-Rynkiewicz Tacjana , Witkowska Anna , Głuch Jerzy , Adamowicz Marcin

Identyfikatory

DOI

10.2478/pomr-2022-0050

• Języki publikacji: EN

Abstrakty

EN

Very often, the operation of diagnostic systems is related to the evaluation of process functionality, where the diagnostics is carried out using reference models prepared on the basis of the process description in the nominal state. The main goal of the work is to develop a hierarchical gas turbine reference model for the estimation of start-up parameters based on multi-layer perceptron neural networks. A functional decomposition of the gas turbine start-up process was proposed, enabling a modular analysis of selected parameters of the process. Real data sets obtained from observations of the turbo-generator set located on a North Sea platform were used.

Słowa kluczowe

EN

industrial gas turbine; start-up monitoring; artificial neural network; hierarchical system

• Wydawca: Gdańsk University of Technology, Faculty of Ocean Engineering & Ship Technology
• Czasopismo: Polish Maritime Research
• Rocznik: 2022
• Tom: nr 4
• Strony: 123--131
• Opis fizyczny: Bibliogr. 24 poz., rys., tab.

Twórcy

autor

Niksa-Rynkiewicz Tacjana

• Gdańsk University of Technology Faculty of Mechanical Engineering and Ship Technology Institute of Ocean Engineering and Ship Technology Gdańsk Poland

• https://orcid.org/0000-0001-9202-8454

autor

Witkowska Anna

• Gdańsk University of Technology, Faculty of Electrical and Control Egineering Gdańsk Poland

• https://orcid.org/0000-0001-9594-6832

autor

Głuch Jerzy

• Gdańsk University of Technology Faculty of Mechanical Engineering and Ship Technology Institute of Energy Gdańsk Poland

• https://orcid.org/0000-0002-0855-509X

autor

Adamowicz Marcin

Bibliografia

• 1. O. Cherednichenko, S. Serbin, and M. Dzida, “Application of Thermo-chemical Technologies for Conversion of Associated Gas in Diesel-Gas Turbine Installations for Oil and Gas Floating Units,” Polish Marit. Res., vol. 26, no. 3, 2019, doi: 10.2478/pomr-2019-0059.
• 2. S. Serbin, K. Burunsuz, M. Dzida, J. Kowalski, and D. Chen, “Investigation of ecological parameters of a gas turbine combustion chamber with steam injection for the floating production, storage, and offloading vessel,” Int. J. Energy Environ. Eng., 2021, doi: 10.1007/s40095-021-00433-w.
• 3. V. Panov, “GasTurbolib-Simulink Library for Gas Turbine Engine Modelling”, Proceedings of ASME Turbo Expo 2009, GT2009, June 8-12, Orlando, Florida, USA, 2009, doi.org/10.1115/GT2009-59389.
• 4. S. Serbin, N. Washchilenko, M. Dzida, and J. Kowalski, “Parametric analysis of the efficiency of the combined gas-steam turbine unit of a hybrid cycle for the FPSO vessel”, Polish Marit. Res., vol. 28, no. 4, 2022, doi: 10.2478/ pomr-2021-0054.
• 5. H. E. M. A. Shalan, M. A. Moustafa Hassan, A. B. G. Bahgat, “Comparative Study on Modelling of Gas Turbines in Combined Cycle Power Plants”, Proceedings of the 14th International Middle East Power Systems Conference (MEPCON’10), Cairo University, Egypt, December 19-21, Paper ID 317, 2010.
• 6. E. Tsoutsanis, N. Meskin, M. Benammar, K. Khorasani, “Dynamic Performance Simulation of an Aeroderivative Gas Turbine Using the Matlab Simulink Environment”, Proceedings of the ASME International Mechanical Engineering Congress & Exposition, November 2013, California, USA, 2013, doi.org/10.1115/IMECE2013-64102.
• 7. A. Lazzaretto, A. Toffolo, “Analytical and Neural Network Models for Gas Turbine Design and Off-Design Simulation”, Int. J. Appl. Thermodynam., vol. 4, no. 4, pp. 173–82, 2001.
• 8. S. Serbin, B. Diasamidze, and M. Dzida, “Investigations of the working process in a dual-fuel low-emission combustion chamber for an FPSO gas turbine engine” Polish Marit. Res., vol. 27, no. 3, 2020, doi: 10.2478/pomr-2020-0050.
• 9. A. Witkowska, T. Niksa-Rynkiewicz, “Dynamically positioned ship steering making use of backstepping method and artificial neural networks”, Polish Marit. Res., vol. 4, pp. 5-12, 2018, doi.org/10.2478/pomr-2018-0126.
• 10. M. Seera, C. P. Lim, S. Nahavandi, C. K. Loo, “Condition monitoring of induction motors: A review and an application of an ensemble of hybrid intelligent models”, Expert Systems with Applications, vol. 41, no. 10, pp. 4891-4903, 2014, doi. org/10.1109/INDEL.2016.7797800.
• 11. T. Niksa-Rynkiewicz, N. Szewczuk-Krypa, A. Witkowska, K. Cpałka, M. Zalasiński, A. Cader, “Monitoring Regenerative Heat Exchanger in Steam Power Plant by Making Use of the Recurrent Neural Network”, Journal of Artificial Intelligence and Soft Computing Research, vol. 11, pp. 143-155, 2011, https://doi.org/10.2478/jaiscr-2021-0009.
• 12. H. R. Depold, F. D. Gass, “The application of expert systems and neural networks to gas turbine prognostics and diagnostics”, J. of Engineering for Gas Turbines and Power, vol. 121, no. 4, pp. 607-612, 1999, doi.org/10.1115/1.2818515.
• 13. H. Asgari, X. Chen, R. Sainudiin, M. Morini, M. Pinelli, P. R. Spina, M. Venturini, “Modeling and Simulation of the Start-Up Operation of a Heavy-Duty Gas Turbine by Using NARX Models” Proceedings of the ASME Turbo Expo 2014: Turbine Technical Conference and Exposition. Volume 3A: Coal, Biomass and Alternative Fuels; Cycle Innovations; Electric Power; Industrial and Cogeneration. Düsseldorf, Germany. June 16–20, 2014, V03AT21A003 ASME, doi. org/10.1115/GT2014-25056.
• 14. W. Molla Salilew, Z. Ambri, A. Karim, A. T. Baheta, “Review on gas turbine condition based diagnosis method” Materials Today: Proceedings, 2021, ISSN 2214-7853, doi. org/10.1016/j.matpr.2020.12.1049.
• 15. J. S. R. Jang, “ANFIS: adaptive-network-based fuzzy inference system” IEEE Transactions on Systems, Man and Cybernetics, vol. 23, no. 3, pp. 665-685, 1993, doi: 10.1109/21.256541.
• 16. B. Shahriari, A. N. Shahrbabaki, A. Shahriari, “Gas turbine fault detection and isolation using adaptive neurofuzzy inference system (ANFIS)” Advanced Materials Research, vol. 1016, pp. 721-725, 2014, doi: 10.4028/www.scientific. net/AMR.1016.721.
• 17. Yu Zhang, S. Cruz-Manzo, A. Latimer, “Start-up vibration analysis for novelty detection on industrial gas turbines” 2016, doi: 10.1109/INDEL.2016.7797800.
• 18. S. Jafari; S. A. Miran Fashandi, T. Nikolaidis, “Modeling and Control of the Starter Motor and Start-Up Phase for Gas Turbines” Electronics, vol. 8, p. 363, 2019, doi.org/10.3390/ electronics8030363.
• 19. N. Chiras, C. Evans, D. Rees, “Nonlinear Gas Turbine Modelling Using Feedforward Neural Networks” 2002, doi: 10.1115/GT2002-30035.
• 20. T. Brotherton, G. Jahns, J. Jacobs and D. Wroblewski, “Prognosis of faults in gas turbine engines” 2000 IEEE Aerospace Conference. Proceedings (Cat. No.00TH8484), vol. 6, pp. 163-171, 2000, doi: 10.1109/AERO.2000.877892.
• 21. M. Adamowicz, G. Żywica, “Advanced Gas Turbines Health Monitoring Systems” Diagnostyka, vol. 19, no. 2, pp. 77–87, 2018, doi: 10.29354/diag/89730.
• 22. J. Lee, F. Wu, W. Zhao, M. Ghaffari, L. Liao, D. Siegel, “Prognostics and health management design for rotary machinery systems – Reviews, Methodology and Applications” Mechanical Systems and Signal Processing, vol. 42, pp. 314–334, 2014, doi.org/10.1016/j.ymssp.2013.06.004.
• 23. K. Kanelopoulos, A. Stamatis, K. Mathioudakis, “Incorporating Neural Networks into Gas Turbine Performance Diagnostics”, ASME paper, 97-GT-35, 1997, doi.org/10.1115/97-GT-035.
• 24. M. D. Zeiler, Adadelta: “An Adaptive Learning Rate Method”, 2012.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).