Development of an optimized artificial neural network model for combined heat and power micro gas turbines

Applied Energy

Volumn 108, Issue , 2013, Pages 137-148

  Nikpey, H ^a   Assadi, M ^a,b   Breuhaus, P ^b  

^a UNIVERSITY OF STAVANGER   (Norway)

^b INTERNATIONAL RESEARCH INSTITUTE OF STAVANGER   (Norway)

Author keywords

ANN modeling; CHP; Measurement; Micro gas turbine; Monitoring; Sensitivity analysis

Indexed keywords

BACKPROPAGATION ALGORITHMS; COGENERATION PLANTS; DIGITAL STORAGE; FORECASTING; MATHEMATICAL MODELS; MEASUREMENTS; MONITORING; NETWORK LAYERS; NEURAL NETWORKS; OPTIMIZATION; POWER GENERATION; SENSITIVITY ANALYSIS;

ANN MODELING; ARTIFICIAL NEURAL NETWORK MODELS; CHP; COMBINED HEAT AND POWER; COMPONENT CHARACTERISTICS; MICRO GAS TURBINES; MONITORING APPLICATIONS; MULTI-LAYER FEED FORWARD;

GAS TURBINES;

ACCURACY ASSESSMENT; ARTIFICIAL NEURAL NETWORK; BACK PROPAGATION; COMBINED HEAT AND POWER; ELECTRICITY GENERATION; ELECTRICITY SUPPLY; ERROR ANALYSIS; MONITORING SYSTEM; NUMERICAL MODEL; PARAMETERIZATION; PREDICTION; SENSITIVITY ANALYSIS; TURBINE;

CHANDIPURA VIRUS;

EID: 84876309512     PISSN: 03062619     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apenergy.2013.03.016     Document Type: Article

References (28)

1. International Energy Agency. Are we entering a golden age of gas? 2011. < [accessed: 06/2012]. http://www.iea.org/publications/freepublications/.>
2. International Energy Agency. World energy outlook; 2008. p. 143. < [accessed: 06/2012]. http://www.iea.org/publications/freepublications/.>
3. Pilavachi P.A. Mini- and micro-turbines for combined heat and power. J Appl Therm Eng 2002, 22:2003-2014.
4. Palmé T., Fast M., Thern M. Gas turbine sensor validation through classification with artificial neural networks. J Appl Energy 2011, 88:3898-3904.
5. Ogaji S.O.T., Singh R. Advanced engine diagnostics using artificial neural networks. J Appl Soft Comput 2003, 3:259-271.
6. Pinelli M., Spina P.R. Gas turbine field performance determination: sources of uncertainties. J Eng Gas Turbine Power 2002, 124:155-160.
7. Yepifanov S, Loboda I. Gas path model identification as an instrument of gas turbine diagnosing. In: ASME turbo expo, paper no. GT2003-38365; 2003.
8. Kamboukos Ph., Mathioudakis K. Comparison of linear and non-linear gas turbine performance diagnostics. J Eng Gas Turbine Power 2005, 127:49-56.
9. Palmé T, Breuhaus P, Assadi M, Klein A, Kim M. New alstom monitoring tools leveraging artificial neural network technologies. In: ASME turbo expo, paper no. GT2011-45990; 2011.
10. Palmé T, Breuhaus P, Assadi M, Klein A, Kim M. Early warning of gas turbine failure by nonlinear feature extraction using an auto-association neural network approach. In: ASME turbo expo, paper no. GT2011-45991; 2011.
11. Smrekar J., Assadi M., Fast M., Kuštrin I., De S. Development of artificial neural network model for a coal-fired boiler using real plant data. J Energy 2009, 34:144-152.
12. Smrekar J., Pandit D., Fast M., Assadi M., De S. Prediction of power output of a coal-fired power plant by artificial neural network. J Neural Comput Appl 2010, 19:725-740.
13. De S., Kaiadi M., Fast M., Assadi M. Development of an artificial neural network model for the steam process of a coal biomass cofired combined heat and power (CHP) plant in Sweden. J Energy 2007, 32:2099-2109.
14. Fast M., Assadi M., De S. Development and multi-utility of an ANN model for an industrial gas turbine. J Appl Energy 2009, 86:9-17.
15. Mathioudakis K., Stamatis A., Tsalavoutas A., Aretakis N. Performance analysis of industrial gas turbines for engine condition monitoring. Proc Inst Mech Eng Part A J Power Energy 2001, 215:173-184.
16. Boccaletti C., Cerri G., Seyedan B. A neural network simulator of a gas turbine with a waste heat recovery section. J Eng Gas Turbine Power 2001, 123:371-376.
17. Mesbahi E, Assadi M, Torisson T, Lindquist T. A unique correction technique for evaporative gas turbine (EvGT) parameters. In: ASME turbo expo; 2001.
18. Bartolini C.M., Caresana F., Comodi G., Pelagalli L., Renzi M., Vagni S. Application of artificial neural networks to micro gas turbines. J Energy Convers Manage 2011, 52:781-788.
19. Yoon J.E., Lee J.J., Kim T.S., Sohn J.L. Analysis of performance deterioration of a micro gas turbine and the use of neural network for predicting deteriorated component characteristics. J Mech Sci Technol 2008, 22:2516-2525.
20. Sisworahardjo N.S., El-Sharkh M.Y., Alam M.S. Neural network controller for microturbine power plants. J Electr Power Syst Res 2008, 78:1378-1384.
21. Chang YZ, Hung KT, Shih HY. Optimizing the swiss-roll recuperator of an innovative micro gas turbine by a surrogate neural network and the multi-objective direct algorithm. In: ASME turbo expo, paper no. GT2008-50762; 2008.
22. Verstraete T, Alsalihi Z, Van Den Braembussche RA. Multidisciplinary optimization of a radial compressor for micro gas turbine applications. In: ASME turbo expo, paper no. GT2007-27484; 2007.
23. Turbec AB. D10293 technical description - T100 microturbine CHP system. Ver. 4.0, English; 2000.
24. Haykin S. Neural networks and learning machines 2009, Pearson Education, Inc., New Jersey. 3rd ed.
25. Bishop M.C. Neural networks for pattern recognition 1995, Oxford University Press, Inc., New York.
26. Loboda I, Feldshteyn Y, Ponomaryov V. Neural network for gas turbine fault identification: multilayer perceptron or radial basis network? In: ASME turbo expo, paper no. GT2011-46752; 2011.
27. Principe J.C., Euliano N.R., Lefebvre W.C. Neural and adaptive systems: fundamentals through simulations 2000, Wiley, New York.
28. Saravanamuttoo H.I.H., Rogers G.F.C., Cohen H., Straznicky P.V. Gas turbine theory 2009, Prentice Hall. 6th ed.