Research on reactive power optimization based on immunity genetic algorithm

Authors:
Keyan Liu;Wanxing Sheng;Yunhua Li
Affiliations:
Beijing University of Aeronautics and Astronautics, Beijing, China, Beijing, China;China Electric Power Research Institute, Beijing, China, Beijing, China;Beijing University of Aeronautics and Astronautics, Beijing, China, Beijing, China
Venue:
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Year:
2006

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Guest editorial special issue on artificial immune systems

IEEE Transactions on Evolutionary Computation

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Abstract

This paper proposed a new kind of immune genetic algorithm (IGA) according to the current algorithms solving the reactive power optimization. The hybrid algorithm is applied in reactive power optimization of power system. Adaptive crossover and adaptive mutation are used according to the fitness of individual. The substitution of individuals is implemented and the multiform of the population is kept to avoid falling into local optimum. The decimal integer encoding and reserving the elitist are used to improve the accuracy and computation speed. The flow chart of improved algorithm is presented and the parameter of the immune genetic algorithm is provided. The procedures of IGA algorithm are designed. A standard test system of IEEE 30-bus has been used to test. The results show that the improved algorithm in the paper is more feasible and effective than current known algorithms.