TY - GEN
T1 - Assessment of genetic algorithm selection, crossover and mutation techniques in reactive optimization power
AU - Al-Hajri, Muhammad Tami
AU - Abido, M. A.
PY - 2009
Y1 - 2009
N2 - In this paper assessment of different Genetic Algorithm (GA) selection, crossover and mutation techniques in term of convergence to the optimal solution for single objective reactive power optimization problem is presented and investigated. The problem is formulated as a nonlinear optimization problem with equality and inequality constraints. Also, in this paper a simple cost appraisal for the potential annual cost saving of these GA techniques due to reactive power optimization will be conducted.Wale & Hale 6 bus system was used in this paper study.
AB - In this paper assessment of different Genetic Algorithm (GA) selection, crossover and mutation techniques in term of convergence to the optimal solution for single objective reactive power optimization problem is presented and investigated. The problem is formulated as a nonlinear optimization problem with equality and inequality constraints. Also, in this paper a simple cost appraisal for the potential annual cost saving of these GA techniques due to reactive power optimization will be conducted.Wale & Hale 6 bus system was used in this paper study.
UR - https://www.scopus.com/pages/publications/70449889886
U2 - 10.1109/CEC.2009.4983055
DO - 10.1109/CEC.2009.4983055
M3 - Conference contribution
AN - SCOPUS:70449889886
SN - 9781424429592
T3 - 2009 IEEE Congress on Evolutionary Computation, CEC 2009
SP - 1005
EP - 1011
BT - 2009 IEEE Congress on Evolutionary Computation, CEC 2009
ER -