Assessment of genetic algorithm selection, crossover and mutation techniques in reactive power optimization

Authors:
Muhammad Tami Al-Hajri;M. A. Abido
Affiliations:
Power Distribution Department, Saudi Aramco, Dhahran, East Province, Saudi Arabia;Electrical Engineering Department, King Fahad University of Petroleum & Minerals, Dhahran, Saudi Arabia
Venue:
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Year:
2009

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Abstract

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.