Comparative study of feature extraction techniques for neural network classifier [power system simulation]

This paper compares two feature extraction techniques for neural network classifiers. The techniques evaluated are used for the dynamic security assessment of power systems. The feature extraction methods are used to map the observation vectors from the measurement space into a lower dimension feature space. The patterns in the feature space can then be utilized to train a neural network (NN) classifier. The NN classifier is used to classify a given power system into either a “secure” or “insecure” class. The feature vectors not only represent a reduction in dimensionality, but also lead to an improvement in class dispersion, and hence to a better classification. Two feature extraction algorithms, the minimum entropy method and the Karhunen-Loe´ve expansion, have been studied to examine their intraset clustering and interset class dispersion. A NN pattern classifier system is developed to illustrate the feasibility of classifying any given operating condition into either secure, or insecure class. Security assessment data from two utility power systems are used to test the proposed techniques