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Doctoral Dissertation Announcement
Candidate: Qais H. Alsafasfeh
Doctor of Philosophy
Department: Electrical and Computer Engineering
Title: Pattern Recognition for Fault Detection, Classification, and Localization in Electrical Power Systems
Dr. Ikhlas Abdel-Qader, Chair
Dr. Johnson Asumadu
Dr. Azim Houshyar
Dr. Ahmad Harb
Date: Monday, May 24, 2010 10:00 a.m. - Noon
College of Engineering and Applied Sciences, Room D212
The longer it takes to identify and repair a fault, the more damage may result in the electrical power system, especially in periods of peak loads, which could lead to the collapse of the system, causing the power outage to extend for a longer period and larger parts of the electrical network. Reducing the outage time and immediate restoration of service can be achieved if the fault type and location are determined in a timely and precise manner.
An integrated algorithm that is based on generating unique signatures from the electric current signal to detect, classify, and localize a fault in one relay is developed. This protection framework will be general enough to be deployed at any end of a transmission line without the need for data communication between the two ends. The proposed framework and algorithm in this dissertation will use values of each phase current during a (¼)th of a cycle and will integrate the symmetrical components technique using the fault signal to generate unique signatures of events. The Principal Component Analysis (PCA) technique is used to declare, identify, and classify a fault using these signatures in the training data set. The fault location is also determined by combining the curve fitting polynomial technique with the unique distance indices that are generated from the signatures already determined. This framework is implemented and simulated using MATLAB and Power System Computer Aided Design (PSCAD) simulation system and tested using several network scenarios including 3- and 6-Bus Electrical Networks, and the IEEE 14 Bus. This framework, as demonstrated by the results presented in the dissertation, has the following significant contributions: 1) it can detect and classify any type of fault using novel signatures approach; 2) it can determine the fault location with a significantly high accuracy; 3) it can distinguish between a real fault and a transient event; and 4) it can detect and classify high impedance faults, making it suitable for use in both transmission and distribution systems.