Empirical Mode Decomposition and Cascade Feed-Forward Artificial Neural Network Based Intelligent Fault Classifier

Abstract

Recent years have witnessed a fast emergence of smart grids, since the availability of electric energy has become a crucial issue in power system engineering. In fact, smart grids will allow for good energy management while ensuring a better control and protection of electric power system components. In this respect, here we propose an intelligent fault classifier (IFC) based on the empirical mode decomposition (EMD) and cascade feedforward neural networks (CFFANN). The EMD is used to decompose sending-end voltages into intrinsic mode functions (IMFs). Then the Hilbert transform (HT) is utilized to extract features from these IMFs. After this step, we selected relevant attributes from the extracted features. CFFANN is used to make the learning process faster. This neural network is introduced to classify all the ten main faults that may occur in a transmission line. The IFC conception is validated using MATLAB/SIMULINK software. The obtained results show that the IFC is a reliable and robust system.