Title :
Reduced order modeling using a genetic algorithm
Author :
Maust, Reid S. ; Feliachi, Ali
Author_Institution :
Dept. of Comput. Sci. & Electr. Eng., West Virginia Univ., Morgantown, WV, USA
Abstract :
Uses a genetic algorithm (GA) to find a reduced-order model to approximate a linear system. To test the validity of the technique, the GA is applied to an example system from the literature. The GA´s solution is observed to agree closely with the optimal solution. Then, the GA is applied to approximating a large power system, for which the analytic methods become unwieldy. The GA´s solution is seen to outperform a commonly used suboptimal method
Keywords :
genetic algorithms; power system analysis computing; reduced order systems; genetic algorithm; large power system; linear system; reduced order modeling; Computer science; Differential equations; Genetic algorithms; Linear approximation; Nonlinear equations; Power system modeling; Read only memory; Reduced order systems; Riccati equations; State estimation;
Conference_Titel :
System Theory, 1998. Proceedings of the Thirtieth Southeastern Symposium on
Conference_Location :
Morgantown, WV
Print_ISBN :
0-7803-4547-9
DOI :
10.1109/SSST.1998.660021
