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A Dynamic Model for a Gas-Liquid Corona Discharge Using Neural Networks

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4 Author(s)
Hosny, A.A. ; Dept. of Electr. Eng., SUNY - Univ. at Buffalo, Buffalo, NY ; Hopkins, D.C. ; Gay, Z.B. ; Safiuddin, M.

This paper presents a novel dynamic nonlinear model for pulsed corona discharge using backpropagation neural networks. The Levenberg-Marquardt training algorithm, which is perfectly suitable for fitting functions, is employed. The developed model is based on the voltage-current characteristics of an actual hybrid-series reactor and takes the practical constrains associated with a real system into account. The validity and accuracy of the model have been tested in the Electromagnetic Transients Program, using MODELS language and a TACS-91 time-variant controlled resistor. The results clearly demonstrate that the BPNN-based model is very robust and effective in emulating the chaotic performance for pulsed corona discharge using backpropagation neural networks.

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Power Delivery, IEEE Transactions on  (Volume:24 ,  Issue: 3 )