Date of Award
Master of Science in Engineering
Electrical and Computer Engineering
Dr. Ikhlas Abdel-Qadar
Dr. Johnson Asumadu
Dr. Janos L. Grantner
Masters Thesis-Open Access
Electrical transmission lines are prone to faults and failures. When a fault occurs, it is impossible most of the times to fix it manually. Many methods have been adopted in the past in order to serve the purpose as fault diagnosing application. In this thesis, I discuss the method of Support Vector Machine (SVM) for fault diagnosis. SVM has the edge of good generalization over other fault diagnosing applications because it is based on pattern recognize algorithms. The aim is to classify the type of fault in the lines. Furthermore, in this work, the current and voltage of each phase are sampled, calculated and then utilized as an optimal learning pattern. Using this method, experimental simulations will show that SVM can identify each class accurately in comparison to previously used methods such as; Expert System, Artificial Neural Network, Petri Net, Fuzzy Theory. The results of simulation tests demonstrate the effectiveness of the proposed method to automatic fault diagnosis.
Jia, Zhuokang, "Power Transmission Line Fault Classification Using Support Vector Machines" (2012). Master's Theses. 104.