Transient stability evaluation of electric power systems using a fuzzy Perceptron algorithm

The stability evaluation is performed rapidly by using a pattern recognition approach incorporating fuzzy membership functions. The algorithm is used to derive a classifier that classifies the system operating states as either stable or unstable. The feature selection is made by using a suitable function and a set of data representing the transient behavior of a network after the occurrence of specific large disturbances. The results obtained are illustrated by the analysis of a sample power system.<>