摘要
研究了电力系统的无功优化功问题,给出了结合电力市场实行的无功优化目标函数。在分析了遗传算法和蚁群算法各自优缺点的基础上,将遗传算法与蚁群算法融合,利用遗传算法的交叉、变异操作产生蚁群算法新的搜索路径,以此提高混合智能算法的全局搜索能力和收敛速度,并将混合智能算法应用于实例进行仿真。仿真结果表明,该混合智能算法具有快速的收敛速度和优良的全局优化能力。
In this paper,the reactive power optimization of power system is researched and the objective function of reactive power optimization combined with electricity market is given.Based on the analysis of the advantages and disadvantages of genetic algorithm and ant colony algorithm respectively,an amalgamation of the two algorithms is given by using crossover and mutation operations of genetic algorithm to generate new searching path for ant colony algorithm,which can be used to improve the global search ability and convergence speed of hybrid intelligent algorithm.Then this hybrid intelligent algorithm applied in a instance is simulated.The simulation results show that the hybrid intelligent algorithm has fast convergence speed and fine global optimization ability.
出处
《江西电力职业技术学院学报》
CAS
2012年第1期1-4,共4页
Journal of Jiangxi Vocational and Technical College of Electricity
关键词
无功优化
遗传算法
蚁群算法
混合智能算法
reactive power optimization
genetic algorithm
ant colony algorithm
hybrid intelligent algorithm
