Recurrent neurofuzzy network in thermal modeling of power transformers

IEEE Transactions on Power Delivery

Volumn 22, Issue 2, 2007, Pages 904-910

  Hell, Michel ^a   Costa Jr , Pyramo ^b   Gomide, Fernando ^a  

^a UNIVERSITY OF CAMPINAS   (Brazil)

^b PONTIFICAL CATHOLIC UNIVERSITY OF MINAS GERAIS   (Brazil)

Author keywords

Power transformers; Recurrent neurofuzzy networks (RNFNs); Thermal modeling

Indexed keywords

COMPUTER SIMULATION; FUZZY NEURAL NETWORKS; MULTILAYER NEURAL NETWORKS; RADIAL BASIS FUNCTION NETWORKS; RECURRENT NEURAL NETWORKS; TEMPERATURE DISTRIBUTION;

MULTILAYER FEEDFORWARD NETWORKS; RECURRENT NEUROFUZZY NETWORKS; THERMAL MODELING;

POWER TRANSFORMERS;

EID: 34147147590     PISSN: 08858977     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPWRD.2006.874613     Document Type: Article

References (14)

1. G. W. Swift et al., "Adaptative transformer thermal overload protection," IEEE Trans. Power Del., vol. 16, no. 4, pp. 516-521, Oct. 2001.
2. J. Declercq, W. Van Der Veken, M. Baelmans, and S. Van Mileghem, "Detailed modelling of thermal transients in power transformers," presented at the New Trends in Hot Spot Temperature Rise Modelling in Power Transformers, Thailand, 1999.
3. V. Galdi, L. Ippolito, A. Piccolo, and A. Vaccaro, "Neural diagnostic system for transformer thermal overload protection," Proc. Inst. Elect. Eng., Elect. Power Appl., vol. 147, no. 5, pp. 415-421, Sep. 2000.
4. P. Daponte, D. Grimaldi, A. Piccolo, and D. Villacci, "A neural diagnostic system for the monitoring of transformer heating," Measurement, vol. 18, no. 1, pp. 35-46, May 1996.
5. W. Maydl and B. Sick, "Recurrent and non-recurrent dynamic network paradigms: A case study," in Proc. IEEE/INNS/ENNS Int. Joint Conf. Neural Networks, Como, Italy, Jul. 2000, vol. 6, pp. 73-78.
6. G. Goodwin and R. Payne, Dynamic System Identification: Experiment Design and Data Analysis. New York: Academic, 1977.
7. D. Villaci, G. Bontempi, A. Vaccaro, and M. Birattari, "The role of learning methods in the dynamic assessment of power components loading capability," IEEE Trans. Ind. Eletron., vol. 52, no. 1, pp. 280-290, Feb. 2005.
8. A. Buckley and Y. Hayashi, "Fuzzy neural networks: A survey," Fuzzy Sets Syst., vol. 66, no. 1, pp. 1-13, Aug. 1994.
9. C. T. Lin and C. S. Lee, Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems. Upper Saddle River, NJ: Prentice-Hall, 1996.
10. R. Ballini and F. Gomide, "Learning in recurrent, hybrid neurofuzzy networks," in Proc. IEEE Int. Conf. Fuzzy Syst., May 2002, vol. 1, pp. 785-791.
11. J. Zhang and A. J. Morris, "Recurrent neuro-fuzzy networks for nonlinear process modeling," IEEE Trans. Neural Netw., vol. 10, no. 2, pp. 313-326, Feb. 1999.
12. W. Pedrycz and F. Gomide, An Introduction to Fuzzy Sets: Analysis and Design. Cambridge, MA: MIT Press, 1998.
13. W. Caminhas, H. Tavares, F. Gomide, and W. Pedrycz, "Fuzzy set based neural networks: Structure, learning and application," J. Advanced Comput. Intell. Intell. Inform., vol. 3, no. 3, pp. 151-156, Jun. 1999.
14. M. Figueiredo, R. Ballini, S. Soares, M. Andrade, and F. Gomide, "Learning algorithms for a class of neurofuzzy network and application," IEEE Trans. Syst., Man Cybern. C, Appl. Rev., vol. 34, no. 6, pp. 293-301, Aug. 2004.