基于改进蝙蝠算法的配电网故障区段定位

提出一种将自适应理论、差分进化和蝙蝠优化算法结合的改进蝙蝠算法，解决电力系统配电网故障区段定位典型的含0-1离散约束条件及逻辑求值的最优化问题。将蝙蝠算法的全局寻优能力用于配电网故障区段的搜索和定位，在寻优过程中引入差分进化算法，并对算法的变异和交叉等操作进行自适应优化处理，解决传统蝙蝠算法容易陷入局部最优的缺陷。算例仿真结果表明，该算法能准确快速地定位配电网故障区段，并具有良好的容错性。

Fault Section Location of Distribution Network Based on Improved Bat Algorithm

An improved bat algorithm combining adaptive theory, differential evolution and bat optimization algorithm is proposed to solve the typical 0-1 discrete constraints and optimization problem of logic evaluation for fault section location in power distribution network. The global optimization ability of bat algorithm is used to iteratively optimize the fault section of distribution network. At the same time, aiming at the disadvantage that single algorithm is easy to fall into local optimum, differential evolution algorithm is introduced into the process of bat algorithm, and the mutation and crossover operation of the algorithm are adaptively optimized. The simulation results indicate that the algorithm can accurately and effectively locate the fault section of distribution network, and has better fault tolerance.