Gas Turbine Modeling Based on Fuzzy Clustering Algorithm Using Experimental Data

References

• Ablay, G. 2013. A modeling and control approach to advanced nuclear power plants with gas turbines. Energy Conversion and Management 76 (December):899–909. doi:10.1016/j.enconman.2013.08.048.
   Web of Science ®Google Scholar
• Abonyi, J., R. Babuska, and F. Szeifert. 2002. Modified Gath-Geva fuzzy clustering for identification of Takagi-Sugeno fuzzy models. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 32 (5):612–621. doi:10.1109/TSMCB.2002.1033180.
   PubMed Web of Science ®Google Scholar
• Abonyi, J., B. Feil, S. Nemeth, and P. Arva. 2005. Modified Gath–Geva clustering for fuzzy segmentation of multivariate time-series. Fuzzy Sets and Systems 149 (1):39–56. doi:10.1016/j.fss.2004.07.008.
   Web of Science ®Google Scholar
• Ahmed, H., A. Daoudi, and K. Laroussi. 2011. Application of fuzzy fault detection and isolation approach to the compression system surge. ACTA Press, Control and Intelligent Systems 39 (3):151–158.
   Google Scholar
• Ahmed, H., M. Guemana, and A. Daoudi. 2015. Vibration supervision in gas turbine based on parity space approach to increasing efficiency. Journal of Vibration and Control 21 (8):1622–1632. doi:10.1177/1077546313499927.
   Web of Science ®Google Scholar
• Ahmed, S., N. Shakev, A. V. Topalov, K. Shiev, and O. Kaynak. 2012. Sliding mode incremental learning algorithm for interval Type-2 Takagi-Sugeno-Kang Fuzzy Neural networks. Evolving Systems 3:179–188. doi: 10.1007/s12530-012-9053-6.
   Google Scholar
• Babuška, R., and H. B. Verbruggen. 1995. Identification of composite linear models via fuzzy clustering. Paper presented at Proceedings of the European Control Conference, Rome, Italy, September 5–8.
   Google Scholar
• Barkó, G., J. Abonyi, and J. Hlavay. 1999. Application of fuzzy clustering and piezoelectric chemical sensor array for investigation on organic compounds. Analytica Chimica Acta 398 (2–3):219–226. doi:10.1016/S0003-2670(99)00377-3.
   Web of Science ®Google Scholar
• Berchtold, M., T. Riedel, C. Decker, and K. Van Laerhoven. 2008. Gath-Geva specification and genetic generalization of Takagi-Sugeno-Kang fuzzy models. In IEEE international conference on systems, man and cybernetics, SMC 2008. 595–600. IEEE.
   Google Scholar
• Bezdek, J. C., and J. Douglas Harris. 1979. Convex decompositions of fuzzy partitions. Journal of Mathematical Analysis and Applications 67 (2):490–512. doi:10.1016/0022-247X(79)90039-8.
   Web of Science ®Google Scholar
• Bezdek, J. C., and J. C. Dunn. 1975. Optimal fuzzy partitions: A heuristic for estimating the parameters in a mixture of normal distributions. IEEE Transactions on Computers C-24 (8):835–838. doi:10.1109/T-C.1975.224317.
   Web of Science ®Google Scholar
• Bezdek, J. C., R. Ehrlich, and W. Full. 1984. FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences 10 (2–3):191–203. doi:10.1016/0098-3004(84)90020-7.
   Web of Science ®Google Scholar
• Buckley, J. J. 1992. Universal fuzzy controllers. Automatica 28 (6):1245–1248. doi:10.1016/0005-1098(92)90068-Q.
   Web of Science ®Google Scholar
• Dunn, J. C. 1973. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Journal of Cybernetics and Systems 3 (3):32–57. doi:10.1080/01969727308546046.
   Google Scholar
• Feil, B., J. Abonyi, and F. Szeifert. 2004. Model order selection of nonlinear input–output models––a clustering based approach. Journal of Process Control 14 (6):593–602. doi:10.1016/j.jprocont.2004.01.005.
   Web of Science ®Google Scholar
• Ford, C. L., J. F. Carrotte, and A. D. Walker. 2013. The application of porous media to simulate the upstream effects of gas turbine injector swirl vanes. Computers & Fluids 77 (April):143–151. doi:10.1016/j.compfluid.2013.03.001.
   Web of Science ®Google Scholar
• Frigui, H., and R. Krishnapuram. 1996. A robust clustering algorithm based on competitive agglomeration and soft rejection of outliers. In Proceedings of IEEE computer society conference on computer vision and pattern recognition, 550–555. IEEE.
   Google Scholar
• Frigui, H., C. Hwang, and F. C.-H. Rhee. 2007. Clustering and aggregation of relational data with applications to image database categorization. Pattern Recognition 40 (11):3053–3068. doi:10.1016/j.patcog.2007.02.019.
   Web of Science ®Google Scholar
• Guasch, A., J. Quevedo, and R. Milne. 2000. Fault diagnosis for gas turbines based on the control system. Engineering Applications of Artificial Intelligence 13 (4) 1:477–484. doi:10.1016/S0952-1976(00)00014-2.
   Web of Science ®Google Scholar
• Gustafson, D. E., and W. C. Kessel. 1978. Fuzzy clustering with a fuzzy covariance matrix. In Proceedings of IEEE conference on decision and control including the 17th symposium on adaptive processes, 17, Part: 1, 761–766. IEEE.
   Google Scholar
• Hafaifa, A., F. Laaouad, and K. Laroussi. 2011. A numerical structural approach to surge detection and isolation in compression systems using fuzzy logic controller. International Journal of Control, Automation, and Systems, (IJCAS) 9 (1):69–79. doi:10.1007/s12555-011-0109-3.
   Web of Science ®Google Scholar
• Hafaifa, A., K. Laroussi, and F. Laaouad. 2010. Robust fuzzy fault detection and isolation approach applied to the surge in centrifugal compressor modeling and control. Fuzzy Information and Engineering 2 (1):49–73. doi:10.1007/s12543-010-0037-6.
   Google Scholar
• Jagaduri, R. T., and G. Radman. 2007. Modeling and control of distributed generation systems including PEM fuel cell and gas turbine. Electric Power Systems Research 77 (1):83–92. doi:10.1016/j.epsr.2006.02.003.
   Web of Science ®Google Scholar
• Jurado, F., and J. Carpio. 2006. Improving distribution system stability by predictive control of gas turbines. Energy Conversion and Management 47 (18–19):2961–2973. doi:10.1016/j.enconman.2006.03.028.
   Web of Science ®Google Scholar
• Kim, K. H., H.-J. Ko, and H. Perez-Blanco. 2011. Analytical modeling of wet compression of gas turbine systems. Applied Thermal Engineering 31 (5):834–840. doi:10.1016/j.applthermaleng.2010.11.002.
   Web of Science ®Google Scholar
• Nikpey, H., M. Assadi, P. Breuhaus, and P. T. Mørkved. 2014. Experimental evaluation and ANN modeling of a recuperative micro gas turbine burning mixtures of natural gas and biogas. Applied Energy 117 (March):30–41. doi:10.1016/j.apenergy.2013.11.074.
   Web of Science ®Google Scholar
• Babuška, R.. 1998. Fuzzy modeling for control. International Series in Intelligent Technologies, 12. The Netherlands: Springer Netherlands. ISBN: 0792381548, 9780792381549.
   Google Scholar
• Ruspini, E. H. 1969. A new approach to clustering. Information and Control 15 (1):22–32. doi:10.1016/S0019-9958(69)90591-9.
   Google Scholar
• Setnes, M., R. Babuška, and H. B. Verbruggen. 1998. Complexity reduction in fuzzy modeling. Mathematics and Computers in Simulation 46 (5–6):507–516. doi:10.1016/S0378-4754(98)00079-2.
   Web of Science ®Google Scholar
• Takagi, T., and M. Sugeno. 1985. Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems Man and Cybernetics SMC-15 (1):116–132. doi:10.1109/TSMC.1985.6313399.
   Web of Science ®Google Scholar
• Topalov, A. V. 2011. A new stable on-line learning algorithm for Takagi-Sugeno-Kang type neuro-fuzzy networks. Comptes Rendus De L Academie Bulgare Des Sciennces, (Sciences Et Ingenierie, Automatique Et Informatique), Tome 64 (10):1489–1498.
   Google Scholar
• Topalov, A. V., G. L. Cascella, V. Giordano, F. Cupertino, and O. Kaynak. 2007. Sliding mode neuro-adaptive control of electric drives. IEEE Transactions on Industrial Electronics 54 (1):671–679. doi:10.1109/TIE.2006.888930.
   Web of Science ®Google Scholar
• Tsao, E. C.-K., J. C. Bezdek, and N. R. Pal. 1994. Fuzzy Kohonen clustering networks. Pattern Recognition 27 (5):757–764. doi:10.1016/0031-3203(94)90052-3.
   Web of Science ®Google Scholar
• Vernieuwe, H., B. De Baets, and N. E. C. Verhoest. 2004. Clustering-based identification of TS-models: Comparison on a groundwater model case study. In Proceedings of the IEEE international conference on fuzzy systems, 2004, 3, 1685–1690. IEEE.
   Google Scholar
• Wahba, E. M., and H. Nawar. 2013. Multiphase flow modeling and optimization for online wash systems of gas turbines. Applied Mathematical Modelling 37 (14–15):7549–7560. doi:10.1016/j.apm.2013.01.056.
   Web of Science ®Google Scholar
• Wu, X.-J., Q. Huang, and X.-J. Zhu. 2011. Thermal modeling of a solid oxide fuel cell and micro gas turbine hybrid power system based on modified LS-SVM. International Journal of Hydrogen Energy 36 (1):885–92. doi:10.1016/j.ijhydene.2010.08.022.
   Web of Science ®Google Scholar