An Immune Genetic Algorithm Based on Regulating New Definition of Antibody Density for Power System Reactive Power Optimization
Li Yulong Zong Wei Lü Xianyan He Qiuyu Yuan Qihong Wang Qian (North China Electric Power University Key Laboratory of Power System Protection and Dynamic Security Monitoring and Control under Ministry of Education Beijing 102206 China)
By integrating an immune genetic algorithm,a regulating strategy based on a new definition of antibody density for reactive power optimization in power system is proposed. Firstly,the penalty coefficient adjusted dynamically was applied to the objective function. Then artificial immunity mechanism is introduced into the genetic operation. It is similar to consider to be each antibody on encoding. And then based on similar vector distance choice tactics were proposed, which keep the population diversity. At the same time,it can guarantee algorithms faster convergence. Thus simple genetic algorithm was correspondingly improved. Finally,the proposed method is applied to IEEE30 buses test systems. The result of calculation illustrates that the proposed hybrid strategy is effective. It shows that compared with SGA in the same condition, this approach can reduce remarkably iterative times and improve rate of convergence of the algorithm for reactive power optimization.
【CateGory Index】： TM714.2;TP18