Graduation Semester and Year




Document Type


Degree Name

Doctor of Philosophy in Electrical Engineering


Electrical Engineering

First Advisor

Wei-Jen Lee


The power system industry often operates close to its limits to accommodate the increased demand posing a high risk of blackouts. Power system restoration techniques are utilized post breakout with the focus on load pickup and speedy recovery. In traditional heuristic methods, the load is considered to be constant after it is picked. However, from a system operation point of view, the load varies once picked. This is commonly observed in industrial loads. In Industrial systems, loads, which involve many induction motors, are started in sequence. The high starting currents of the induction motors leads to voltage sags that may affect variable speed drives and cause contactors to drop out. If the load variation and inrush currents are not considered, load at the time of pickup will be significantly underestimated at the time of pickup which might lead to a system re-collapse. Besides, one may have to prioritize the loads to help the operator during restoration. An automatic power system restoration tool is developed by using graph theory to provide an efficient restoration path and considers the priority of the loads, Cold load pickup (CLPU), Inrush currents, and load variation after picking up for a smooth and successful restoration process. Evolution of the smart grid, the Intelligent Electronic Device (IED) has been deployed throughout the power system network for monitoring and control. Therefore, this dissertation takes advantages on the availability of IEDs to report the loads on the feeder right before the system blackout and the real-time load during the system restoration. The industrial system and IEEE 30 bus system are used as a test cases to demonstrate the effectiveness of the proposed methodology.


Restoration, Cold load pickup, Load variation, Inrush currents, Load priority, Industrial system


Electrical and Computer Engineering | Engineering


Degree granted by The University of Texas at Arlington