Date of Award

12-2012

Degree Name

Master of Science in Engineering

Department

Electrical and Computer Engineering

First Advisor

Dr. Ikhlas Abdel-Qadar

Second Advisor

Dr. Johnson Asumadu

Third Advisor

Dr. Janos L. Grantner

Access Setting

Masters Thesis-Open Access

Restricted to Campus until

6-15-2013

Abstract

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.

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