基于改进细菌觅食算法的无功优化

采用改进的细菌觅食(MBFO)算法求解电力系统无功优化问题,引入了步长递减的控制策略,改善了算法前期的全局搜索能力和后期的局部搜索能力;引入了SA-PSO变异算子,从而使个体可以相互交流,并从精英那里得到经验;引入遗传算法的交叉和赌盘选择,保护了精英个体,同时降低了解劣化的概率.以IEEE-30节点为例的算例结果表明,较其他几种优化方法而言,M BFO具有更快的收敛速度和更好的优化效果,故该算法在解决无功优化问题上可行且有效.

Reactive Power Optimization Based on Modified Bacterial Foraging Optimization Algorithm

The reactive power optimization problem of power system is solved by Modified Bacterial Foraging Optimization Algorithm.The algorithm introduces a decreasing step sizes control mode to improve the global search ability in the early stage and the local search ability in the late stage.SA-PSO mutation operator is introduced to make individuals learn from each other;Genetic Algorithm roulette choosing and crossover mutation is introduced to protect the elites and suppress the degeneracy phenomenon.Taking IEEE-30 node test system for examples,the results show that the algorithm is effective in solving reactive power optimization problems and prove that the algorithm convergence speed and optimization algorithm have better performance than other optimization results.