Neural Network Pattern Classifications of Transient Stability and Loss of Excitation for Synchronous Generators

Abstmct The paper preknts a novel AI-ANN neural network global on-line fault detection, pa& tern classification, and relaying detection acheme for synchronous generators in interconnected electric utility networks. The input discriminant vector comprises the dominant FFT frequency spectra of eighteen input variables forming the discriminant diagnostic hyperplane. The on-line ANN based relaying scheme classifies fault existence, fault type as either transient stability or lose of excitation, the allowable critical clearing time, and loss of excitation type as either open circuit or short circuit filed condition. The proposed FFT dominant frequency-based hyperplane diagnostic technique can be easily extended to multi-machine electric interconnected AC systems.