蝠鲼觅食优化算法在配电网故障定位中的应用

Application of manta ray foraging optimization algorithm in fault location of distribution network

针对传统智能优化算法在配电网故障定位应用中存在定位速度较慢和定位精度不高的问题,本文提出了一种新型群体智能算法——即蝠鲼觅食优化算法。文中介绍了蝠鲼的3种觅食行为,并利用Sigmoid函数对个体位置进行二进制处理;构建了单辐射配电网定位模型,并利用该算法对定位模型进行迭代寻优。算例仿真结果表明,该算法在配电网发生单重故障和多重故障的情况下,能够准确地定位出故障区段,具有良好的容错性,收敛速度明显优于遗传算法和二进制粒子群算法。

Aiming at the problems of slow speed and low accuracy of traditional intelligent optimization algorithm in distribution network fault location,a new swarm intelligence algorithm,namely manta ray foraging optimization algorithm,is proposed in this paper.In this paper,three foraging behaviors of manta rays are introduced.Sigmoid function is used for binary processing of individual positions.The location model of single radiation distribution network is constructed and the algorithm is used to optimize the location model iteratively.The simulation results show that the proposed algorithm can locate the fault segment accurately when single or multiple faults occur in distribution network,and has good fault tolerance.The convergence speed is obviously better than genetic algorithm and binary particle swarm optimization algorithm.