Modern electrical engineering education must prepare students to solve a wide range of engineering problems. Thus in the classroom, I place great emphasis on showing students how engineering fundamentals are critical to various broader issues. I employ a variety of teaching strategies, as circumstances seem to warrant. I try to balance theory and practice and use real-world examples that students can relate to. To an extent limited by large class sizes, my goal is to make every effort to actively educate the class as a whole and to build upon the strength of my students. I consider it important for my students to leave with, if nothing more, an understanding of professional and ethical responsibilities and recognition of the need for life-long learning.
Modules currently teaching:
EE6531 Selected Topics in Smart Grid Technologies
EE4511 Sustainable Energy Systems
EE4501 Power System Management & Protection
Robust and reliable power supply is a backbone of any industrial society. This module provides necessary analytical tools required to assess the performance of existing electric power systems under various operating conditions and also to plan the future expansion of such systems. In addition, it introduces various protection schemes employed in the industry. It adequately prepares students seeking employment in the electric energy related industries. The topics covered are: Modeling of power systems: bus admittance and bus impedance matrices, network building algorithms; Load flow studies: problem formulation, computer solution techniques, applications; Fault analysis: symmetrical components, sequence impedance networks, symmetrical and unsymmetrical faults; Protection: components, relay coordination; Protection of distribution systems; Differential, and earth fault protection systems.
EE6701 Evolutionary Computation
This course explores how principles from theories of evolution can be used to construct intelligent systems. Established evolutionary paradigms as well as, significant new developments, including evolutionary algorithms, evolutionary strategies, evolving neural networks, ant-colony optimization, artificial immune systems, and swarm intelligence will be covered. Students will be taught how these approaches identify and exploit biological processes in nature, allowing a wide range of applications to be solved in industry and business. Key problem domains such as multi-objective scheduling, optimization, search, and design will be examined. Students will gain hands-on experience in applying these techniques to real-life problems through project work.
EE5702R – Advanced Power Systems Analysis