Morphology based radon processed neural network for transmission line fault detection

A novel method for classifying transmission line faults is presented in this paper. Mathematical morphology is applied along with radon transform for extracting features needed for fault classification. The features are trained with radial basis network for fault detection. In the present work an HVDC transmission line which is divided into three zones is taken as the work background. Detailed simulation results are shown. The present methodology for fault detection is fast, and the use of morphology operator along with radon transform, reduces the computational complexity compared with other conventional methods in fault detection.