针对复杂配电网的故障恢复问题，提出一种基于图论和改进模糊遗传算法的配电网负荷转供判断方法。首先基于图形库JGraphT进行配电网数据建模，将馈线拓扑结构和元件参数整合在一起，显式地利用配电网拓扑结构，避免了繁琐的节点编码规则和冗杂的邻接表、矩阵存储模式，并基于JGraphT集成的图论算法实现快速的故障区域类型区分、网络拓扑约束判断以及开发出基于图论递归运算的前推回代潮流算法；然后结合电网特点提出改进的模糊遗传算法，动态调整遗传算法的初始解和遗传算子以提高算法寻优性能，通过对模糊输入量和模糊规则进行完善，提高遗传算法的收敛速度，避免不成熟收敛的发生；最后算例结果表明本文算法在潮流计算效率和寻优效率均具有更好的效果。

In view of the fault recovery of complicated power distribution networks，a load transfer method for power distribution networks is proposed based on the graph theory and the improved fuzzy genetic algorithm． First，the to-pological structure and component parameters of feeders are integrated together by means of the JGraphT － based da-ta modeling of power distribution networks，and by taking advantage of the topological structure of power distribution networks explicitly，the tedious node encoding rules and the storage modes in the form of miscellaneous adjacency lists or matrices are avoided． Next，with the help of the graph theory algorithms integrated in JGraphT，different types of fault sections are quickly distinguished and the topological constraint is rapidly determined． Then，a for-ward － backward sweep method based on the recursive graph theory is developed． Finally，an improved fuzzy genet-ic algorithm is proposed according to the characteristics of power distribution networks． In the proposed algorithm，the initial solutions and genetic operators of the genetic algorithm are dynamically adjusted so as to improve the opti-mizing performance of the proposed algorithm，and the fuzzy inputs and fuzzy rules are revised reasonably to im-prove the convergence rate of the genetic algorithm and avoid the premature convergence． Simulation results show that the proposed algorithm is superior to the other methods in terms of power flow calculation and optimization effi-ciency．