基于改进蚁群算法的无功优化研究

Study on reactive power optimization based on improved ant colony algorithm

建立了网损最小的数学模型,对蚁群算法的缺陷进行改进,包括对蚁群搜索到的路径进行排序,自适应调节路径上释放的信息素。同时又在信息素更新机制里引入微分进化算法的发散项,提高算法的收敛速度和全局寻优能力。通过IEEE-30节点的仿真计算,验证了改进蚁群算法在电力系统无功优化领域的可行性和有效性。

This paper establishes the colony algorithm, including sorting mathematical model with the least network loss and improves the defects of ant the path searched by ant colony and the regulation path. Besides, the paper brings in divergence of differential evolution mechanism so as to enhance convergence speed and ability of global optimization. pheromone released on adaptive algorithm to pheromone updating The simulation calculation through IEEE-30 node verifies the feasibility and effectiveness of improved ant colony algorithm in electric power system re- active power optimization.