Optimal methods for fault detection and classifications

Abstract

Detecting fault in transmission line is very important in order to have a well-functioned power system. This is due to the fact that the system will be distorted if there is fault in the transmission line. Occurrence of fault causes the significant difference in terms of the value of current or voltage in the system. There are a few approaches that can be used in order to detect and classify fault in the transmission line. Two methods of fault detection and classification have been used to be analyzed in order to identify both method accuracy and reliability. The two methods are the Wavelet Transform method and the Fuzzy Logic based method. Both methods show their own advantages and disadvantages after simulation have been done. These methods are later being utilized by combining both to create a better version of fault detection and classification method. In this paper, a combined method of Wavelet Transform and Fuzzy Logic based for fault detection and classification model for power systems is developed and simulated. This combined method is later compared to other method under the same category but different perspective and aspect namely the Radial Basis Function Neural Network. Fuzzy Logic Based method and Radial Basis Function Neural Network falls under Artificial Intelligence category for fault classification method. However, the approach used for both method is significantly different.