Neural network pattern classifications of transient stability and loss of excitation for synchronous generators

Abstract

The paper presents a novel AI-ANN neural network global online fault detection, pattern classification, and relaying detection scheme 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 online ANN based relaying scheme classifies fault existence, fault type as either transient stability or loss 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 multimachine electric interconnected AC systems.<>