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ABSTRACT

This paper describes the use of an Artificial Intelligence-Electromagnetic modeling approach for the performance prediction of stand alone synchronous generators during No Break Power Transfer, NBPT, operating conditions. This approach uses Radial Basis Networks, RBN, which have the advantage of not being locked into local minima as do feedforward Neural Networks. The RBNs are simply linear function approximators that use Radial Basis Functions which are powerful techniques for interpolation in multidimensional space. The RBN is used to evaluate the stresses accompanying this mode of operation which may result in the failure of the diodes in the rotating rectifier bridge of the generator brushles field exciter. The modeling approach is applied in a case study of two standalone synchronous generators system for aerospace applications. This study resulted in the prediction of the system performance characteristics including the peak currents and reverse voltages of the rotating diodes. The simulation results were validated by comparison to experimental data.

REFERENCES

• 1: J. W. Park, R. G. Harley, and G. K. Venayagamoorthy, "Comparison of MLP and RBF Neural Networks Using Deviation Signals for On-Line Identification of a Synchronous Generator," IEEE Power Engineering Society Winter Meeting, 2002, Vol. 1, No. 27--31, pp. 274--279, January 2002.
• 2: Nello Cristianini , John Shawe-Taylor, An introduction to support Vector Machines: and other kernel-based learning methods, Cambridge University Press, New York, NY, 1999
• 3: Mehmed Kantardzic, Data Mining: Concepts, Models, Methods and Algorithms, John Wiley & Sons, Inc., New York, NY, 2002
• 4: P. M. Anderson and A. A. Fouad, "Power System Control and Stability," John Wiley Publication, Second Edition, 2003, Hoboken, NJ.
• 5: A. A., Arkadan, R. H., VanderHeiden and J. F. Defenbaugh, "Effects of Forced Power Transfer of High Speed Generator-Load Systems," IEEE Trans. on Energy Conversion, Vol. 11, No. 2, pp. 344--352, June 1996.
• 6: A. A. Arkadan, P. Du, M. Sidani, and M. Bouji, "Performance prediction of SRM drive systems under normal and fault operating conditions using GA-based ANN method" IEEE Trans. on Magnetics, vol. 36, issue 4, pp. 1945--1949, July 2000
• 7: Richard S. Sutton , Andrew G. Barto, Introduction to Reinforcement Learning, MIT Press, Cambridge, MA, 1998
• 8: T. Poggio and F. Girosi, "Networks for Approximation and Learning," Proceeding of the IEEE, 78(9), Sept. 1990, Pages:1484--1487
• 9: Vojislav Kecman, Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models, MIT Press, Cambridge, MA, 2001
• 10: John Moody , Christian J. Darken, Fast learning in networks of locally-tuned processing units, Neural Computation, v.1 n.2, p.281-294, Summer 1989 [doi>10.1162/neco.1989.1.2.281]
• 11: College of Engineering and Information Technology, "Virtual Test Bed," University of South-Carolina, http://vtb.engr.sc.edu/
• 12: N. A. Demerdash, T. M. Hijazi, T. M., and A. A. Arkadan, "Computation of Winding Inductances of Permanent Magnet Brushless DC Motors with Damper Windings by Energy Perturbation," <u>IEEE Transactions on Energy Conversion</u>, Vol. EC-3, No. 3, pp. 705--713, September 1988.
• 13: A. A. Arkadan, and B. W. Kielgas, B. W., "Effects of Force Fitting on the Inductance Profile of a Switched Reluctance Motor," <u>IEEE Transactions on Magnetics</u>, Vol. 29, No. 2, pp. 2006--2009, March 1993.

INDEX TERMS

Keywords:
artificial intelligence, electric machines, electromagnetic analysis, simulation