Spring 2013 textbook list
The Spring 2013 ECE textbook list is available online for students.
Current Prerequisites & Course Offering
For current prerequisites for a particular course, and to view course offerings for a particular semester, see the Virginia Tech Course Timetables.
ECE 6334 Computational Methods in Power Engineering (3C)
This course is designed to introduce various linear and nonlinear program based optimization algorithms that are specially suited for the design, analysis and operation of electric power systems, power processing devices, machines and transformers.
What is the reason for this course?
Various technical areas within power engineering routinely use large scale optimization techniques. These areas include generation expansion planning, bulk power supply systems, hydro-thermal scheduling, power converter design optimization and transformer design. Optimization and computational techniques and algorithms to be discussed in this course will give the student the necessary tools to analyze the design problems listed above.
Typically offered: Fall. Program Area: Power.
Prerequisites: 5324.
Why are these prerequisites or corequisites required?
This course requires a thorough understanding of principles of power system analysis including generation planning, production costing, reliability and stability analysis. This material is covered in EE 5324.
Department Syllabus Information:
Major Measurable Learning Objectives:-
The course should help the student understand the tools required to analyze and evaluate electric power apparatus and systems. In addition the student should also be able to compare the accuracy of results and solution times with respect to the complexity of the algorithms for studying electric power problems.
| Course Topics | |
|---|---|
| Topic | Percentage |
| Power system models | 15% |
| Power processing device models | 15% |
| Transformer models | 15% |
| Electric machine models | 10% |
| Linear programming applications | 15% |
| Quadratic programming applications | 10% |
| Generalized reduced gradient applications | 10% |
| Single and multivariable constrained method applications | 10% |