South Carolina Electric and Gas Distinguished Professor of Electrical and Computer Engineering
Ph.D., 1981 - Iowa State University
Electrical Engineering
M.S., 1978 - Iowa State University
Electrical Engineering
B.S., 1969 - Assiut University, Egypt
Electrical Engineering
Contact Information
Office: 303 Riggs Hall
Office Phone: 864.656.3378
Fax: 864.656.5910
Email: elham.makram@ces.clemson.edu
Academic
Dr. Makram received the MS and Ph.D. degrees from Iowa State University in 1978 and 1981 respectively. From 1970 to 1976, she was an engineer in power system planning in Assuit, Egypt. From 1978 to 1981, she was research assistant at Iowa State University. From 1981 to 1983, she was a Project Engineer at Siemens-Allis, Inc., in Raleigh, NC. From 1983 to 1985, she was an Assistant Professor at North Carolina A&T University. Dr. Makram is an IEEE Fellow, member of ASEE, Sigma Xi, NSPE and CIGRE. She is a registered professional engineer. Her present research interests include computer simulation of power systems, high impedance faults, power system harmonics, and real time application in power systems. She is the recipient of the 1991 Alumni Research Award, the 1992 NSF/FAW award, the 1993 SWE distinguished engineering educator award, the 1994 outstanding faculty award at Clemson University, and the 1998 SCE&G distinguished professor.
Recent research
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Transient constrained Optimal Power Flow
This Project presents the formulation of a transient stability constrained optimal power flow problem. The Taylor series expansion of differential equations method and the value of rotor angles with respect to a center of inertia frame of reference have been used to model the transient stability constraint. The MATLAB optimization toolbox has been used to implement the method. Results on the IEEE 39-bus system show that it is possible to provide a transiently stable dispatch which could prevent a power system from being transiently unstable following a fault.
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Environmental/Economic Generation Scheduling with Hybrid Energy Sources using Evolutionary Algorithm
The environmental/economic dispatch (EED) problem is a multi-objective nonlinear optimization problem with equality and inequality constraints. A multi-objective optimization problem requires a set of trade-off solutions to solve it. The ability of evolutionary algorithms to find the multiple Pareto-optimal solutions in a single simulation run makes it attractive for solving problems with multiple and conflicting objectives. This project presents a genetic algorithm based approach to solve multi-objective hydro-thermal-nuclear-wind generation scheduling problem. The proposed approach minimizes two conflicting objectives while allocating the electricity demand among the committed generating units subject to physical and technological constraints. Operating cost and NOx emissions are the objectives undertaken to be minimized simultaneously. Fuzzy set theory is applied to extract the best compromise non-dominated solution. Simulation results for one hydro unit, two thermal units, one nuclear unit and one wind unit sample power system have been presented to illustrate the performance and applicability of the proposed method.
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On-Line Contingency Analysis using PMU (Phasor Measurement Units) Data
The purpose of this research is to look at on-line data and predict which particular lines within a system have the potential to cause significant damage if they are removed. Off-line data in multiple cases both before and after contingencies occur are used to study a system under different loading conditions. The cases involve both the base case and maximum allowable load increases within the entire system and then within each of the four separate areas. Lines within the system are removed and data is taken if the new system configuration is allowable (no overloaded power flow). From this offline data, patterns are drawn between post contingency data of lines that had significant problems after removal and the data of those same lines before they are removed. The off line data then provides an indicator of which lines within the system will be critical if removed. This indicator is then applied to the PMU data and critical lines are identified before any problems have the chance to occur. This improves the overall security of the power system.