PCA-ANN based algorithm for the determination of asymmetrical network failures of network-connected induction generators

Abstract

Presented in this study is a principal component analysis-artificial neural network based hybrid failure determination system that can make failure determination selectively and rapidly in asymmetrical external failures that might occur on the network side of a grid-connected induction generator. By creating asymmetrical external failures in the developed simulation model, analysis of noisy and unbalanced fluctuations that carry effects of positive, negative and zero sequence in currents were realized. The suggested model depends on entering data taken from the simulation into the artificial neural network model as a training data by being simplified with principal component analysis, in phase-phase, phase-ground and two phase-ground failures. The protection model makes correct classification with acceptable errors in case of above stated failures. However, in current fluctuations caused by sudden load changes and operation under an unbalanced load, it may remain insensitive by behaving selectively.