An Improved Catastrophic Genetic Algorithm and Its Application in Reactive Power Optimization
Open Access
- 1 January 2010
- journal article
- research article
- Published by Scientific Research Publishing, Inc. in Energy and Power Engineering
- Vol. 02 (04), 306-312
- https://doi.org/10.4236/epe.2010.24043
Abstract
This paper presents an Improved Catastrophic Genetic Algorithm (ICGA) for optimal reactive power optimization. Firstly, a new catastrophic operator to enhance the genetic algorithms’ convergence stability is proposed. Then, a new probability algorithm of crossover depending on the number of generations, and a new probability algorithm of mutation depending on the fitness value are designed to solving the main conflict of the convergent speed with the global astringency. In these ways, the ICGA can prevent premature convergence and instability of genetic-catastrophic algorithms (GCA). Finally, the ICGA is applied for power system reactive power optimization and evaluated on the IEEE 14-bus power system, and the application results show that the proposed method is suitable for reactive power optimization in power system.Keywords
This publication has 1 reference indexed in Scilit:
- Adaptive probabilities of crossover and mutation in genetic algorithmsIEEE Transactions on Systems, Man, and Cybernetics, 1994