含分布式电源配电网故障定位交互式二进制蝙蝠算法

针对配电网故障定位二进制粒子群算法定位速度慢且准确度低的缺点，结合群智能平台设计了一种交互式二进制蝙蝠算法(interactive binary bat algorithm，IBBA)，部署在群智能计算节点(computing processing node，CPN)上的IBBA通过CPN自组织、自协作方式与邻居节点交互蝙蝠位置等信息，提高了算法搜索速度和全局搜索能力;通过建立适合多电源配电网的编码方式，引入防伪正系数和假定故障数量的评价函数，增强了算法适用性;搭建IEEE14节点配电网模型，模拟配电网在分布式电源接入和信息畸变等多场景下单点、两点故障状态，通过实验验证了IBBA的有效性。与传统二进制蝙蝠算法、二进制粒子群以及交互式二进制粒子群算法相比，IBBA的收敛性能和容错能力更优，定位准确度较二进制粒子群、交互式二进制粒子群算法分别提高6%和10%。

Interactive binary bat algorithm for fault location in distribution network with distributed generation

Aiming at the shortcomings of the slow and low accuracy of the binary particle swarm algorithm for fault location in the distribution network, an interactive binary bat algorithm (IBBA) was designed in combination with the swarm intelligence platform and deployed on the computing processing node (CPN). The IBBA deployed on the CPN exchanges information such as the location of the bat with neighbor nodes through CPN self-organization and self-cooperation, which improves the algorithm search speed and global search capability. By establishing a coding method suitable for multi-power distribution networks, and introducing anti-counterfeiting positive coefficients and evaluation functions for the number of assumed faults, the applicability of the algorithm is enhanced. Finally, an IEEE14-node distribution network model is built to simulate the single and two fault states of the distribution network in multiple scenarios such as distributed generation access and information distortion, and the effectiveness of IBBA is verified through experiments. Compared with the traditional binary bat algorithm, binary particle swarm and interactive binary particle swarm algorithm, IBBA has better convergence performance and fault tolerance, and the fault location accuracy is improved by 6% and 10% respectively, compared with binary particle swarm and interactive binary particle swarm algorithm.