Date of Award

12-2015

Degree Name

Master of Science in Engineering

Department

Electrical and Computer Engineering

First Advisor

Dr. Johnson Asumadu

Second Advisor

Dr. Ikhlas Abdul-Qader

Third Advisor

Dr. Raghvendra Gejji

Fourth Advisor

Dr. Christopher Cho

Access Setting

Masters Thesis-Open Access

Abstract

This thesis focuses on new approach to detect, classify, and localize the fault in transmission line. Firstly, fault detection was carried out using the New Composite Relay (CR) which, has different characteristics and the ability to detect any type of fault including series faults. Secondly, fault classification was conducted using the Feed Forward Artificial Neural Network (FFANN). In addition, the fault classification led to the investigation of the best use of the FFANN. The data used come from MATLAB/SIMULINK three phase series compensated network. The results obtained using FFANN, were compared with the type of the fault that have been actually applied on the system. Finally, a digital controller was combined with FFANN to appropriately select the type of fault localization that should be used.

Share

COinS