A novel feature selection and extraction method for neural network based transfer capability assessment of power systems

Abstract

A new feature selection and extraction method is presented in this paper for the neural network (NN) based available transfer capability assessment in the deregulated power system. The objective of feature selection and extraction is to speed up the NN training process and to achieve a more accurate NN results. The proposed method is known as the SDFT method in which it is a combination of the sensitivity and discrete Fourier transform methods. The sensitivity analysis is first used in selecting the input features and then followed by the discrete Fourier transform (DFT) method for extracting NN input features. The hypothesis set of pre-selected data performed by the sensitivity method only offers no improvement in the NN training performance in such cases where many features are highly correlated. Thus, the DFT method is considered so as to extract the pre-selected data to a set of meaningful extracted data. To illustrate the effectiveness of the proposed method, a comparative study of the SDFT, DFT and sensitivity methods is made so as to investigate the effectiveness of the methods in extracting and selecting the NN features. In this study, the NN based available transfer capability assessment has been performed on the Malaysian power system.