Application of Zernike Moments in Intelligent Fault Detection of Distribution Networks

This paper presents a new intelligent method for electrical equipment fault detection in power distribution networks based on thermo images by using of Zernike Moments (ZM) for feature extraction and neural network for classifier. There are several types of faults in substations which transformer bushing breakdown, loose connection between conductor and section insulator, fuse deficiency and destruction of cable bug are the most commonly faults in substations that have been selected in this paper. This method has been conducted on practical thermo images from the electrical distribution network of Tehran. Zernike results have been categorized in to four groups according to Zernike orders. Simulation results indicate the validity of approach method with accuracy of 90.3 percentages using of RBF neural network.

Keywords:

Distribution Networks , Intelligent Fault Detection , RBF neural network , Thermo Vision , Zernike Moments