基于免疫进化细菌觅食算法的无功优化

摘要

针对传统细菌觅食算法在优化过程中步长一致、收敛速度较慢的缺陷，提出了一种免疫进化细菌觅食算法（IBFO），并将其用于电力系统无功优化问题上。这种改进的算法赋予了细菌对搜索空间的感知能力，利用灵敏度的概念来调节步长，加快收敛速度；将免疫算法中的克隆选择思想引入算法中，对精英细菌进行克隆、高频变异和随机交叉，提高收敛精度。将 IBFO 算法在IEEE 14、IEEE 30节点标准测试系统中进行了无功优化仿真，结果表明：新算法较其它算法具有较强的全局搜索能力，且收敛速度快、鲁棒性好，可以作为求解电力系统无功优化问题的一种新途径。%According to the drawbacks of the unchanged swim step and slow velocity in the bacterial foraging algorithm,a bacterial foraging optimization algorithm based on immune algorithm (IBFO)was introduced in reactive power optimization.The IBFO gives bacteria the ability of context-aware,and increases the convergence speed by using the sensitivity to adjust the group swim step.The idea of clonal selection in immune algorithm was introduced in bacterial foraging, and the bacterial cloning,high-frequency variation and random crossover of the elite group were achieved so as to increase the accuracy of convergence.The IBFO was implemented on the IEEE 14 bus and IEEE 30 bus system.The results show that the new algorithm has stronger global optimal searching ability,faster convergence rate and better robustness compared with other optimization algorithms.Therefore,as a new approach,it can solve the problem of reactive power optimization in power systems.