Characterization of Stand Alone AC Generators during No-Break Power Transfer using AI-EM Based Approach

Abstract

This paper describes the use of Artificial Intelligence, -Electromagnetic, AI-EM modeling approach for the performance prediction of stand alone synchronous generators during power transfer. This approach uses Radial Basis Function, RBF, based data mining algorithm to evaluate the stresses accompanying the No Break Power Transfer, NBPT. This mode of operation may result in the failure of the diodes in the rotating rectifier bridge of the brushless field exciter. The modeling approach is applied in a case study of a two standalone synchronous generators system. This 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.Abstract-This paper describes the use of Artificial Intelligence, -Electromagnetic, AI-EM modeling approach for the performance prediction of stand alone synchronous generators during power transfer. This approach uses Radial Basis Function, RBF, based data mining algorithm to evaluate the stresses accompanying the No Break Power Transfer, NBPT. This mode of operation may result in the failure of the diodes in the rotating rectifier bridge of the brushless field exciter. The modeling approach is applied in a case study of a two standalone synchronous generators system. This 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.