融合遗传和蚁群算法的三相不平衡配电网无功优化研究

Research on Reactive Power Optimization of Three-phase Unbalanced Distribution Network by Fusion Genetic Algorithm and Ant Colony Algorithm

配电网无功优化是一个多约束、多变量的高度非线性优化问题,而三相不平衡加剧了配电网无功优化的复杂程度。针对三相不平衡配电网无功优化困难的现状,提出了将遗传算法和蚁群算法相融合的智能优化方法,该方法结合了遗传算法全局优化能力强和蚁群算法局部搜索能力强的特点。为验证本方法在三相不平衡配电网无功优化的优势,建立了IEEE33节点三相配电网系统,通过与其它无功优化方法的对比分析验证了本方法的有效性和优越性。可为三相不平衡配电网的无功优化提供有效的参考和指导。

Reactive power optimization of distribution network is a highly nonlinear optimization problem with multiple constraints and variables, and the three - phase imbalance aggravates the complexity of reactive power optimization of distribution network.In view ofthe difficulty of reactive power optimization in three-phase unbalanced distribution network,an intelligent optimization method combining genetic algorithm and ant colony algorithm is proposed in this paper, the method combines the characteristics of genetic algorithm's strong global optimization ability and ant colony algorithm's strong local search ability.In order to verify the advantage of this method in reactive power optimization of three-phase unbalanced distribution network, the IEEE33 node three-phase distribution system is established, the effectiveness and superiority of this method are verified compared with other reactive power optimization methods.The method in this paper can provide effective reference and guidance for reactive power optimization of three-phase unbalanced distribution network loss.