基于改进多元宇宙算法的主动配电网故障定位方法研究

针对现有智能优化算法在求解主动配电网故障定位问题时存在的收敛速度慢、易陷入局部最优解、容错性差、种群质量低等问题，提出一种改进的多元宇宙优化算法(improved multi-verses optimization, IMVO)。首先构建具有容错能力的主动配电网模型，根据故障定位问题的特点对多元宇宙的种群进行离散化编码。其次将自适应精英策略融入改进算法的多元宇宙种群的更迭中，以保证多元宇宙的种群质量。设计基于非线性曲线变化的虫洞存在概率(wormhole existence probability, WEP)与旅行距离率(travel distance rate, TDR)的更新机制，以提高算法前段搜寻相对最优宇宙的能力与后段调整最优探测距离的精度。最后通过自适应突变操作增强改进算法的局部搜索能力，进而提高全局寻优能力。仿真实验结果表明，改进多元宇宙优化算法在单点、多点以及信息畸变故障定位中全局寻优能力显著，相较于其他优化算法在解决配电网故障定位问题上具有更高的准确率与收敛速率。

An active distribution network fault location method based on improved multi-universe algorithm

There are problems of slow convergence, ease of falling into local optima, poor fault tolerance and low population quality of existing intelligent optimization algorithms in solving active distribution network fault location problems. Thus this paper proposes an improved multi-verses optimization (IMVO) algorithm. First, an active distribution network model with fault tolerance is constructed, and the populations of the multiverse are discretized and coded according to the characteristics of the fault location problem. Second, an adaptive elite strategy is incorporated into the update of the multiverse population of the improved algorithm to ensure the population quality of the multiverse; the update mechanism of wormhole existence probability (WEP) and travel distance rate (TDR) based on nonlinear curve change is designed to improve the ability of searching the relative optimal universe in the front part of the algorithm and the accuracy of adjusting the optimal detection distance in the back part. Finally, the local search capability of the improved algorithm is enhanced by an adaptive mutation operation, thus improving the global search capability. The simulation results show that the improved multiverse optimization algorithm has significant global search capability in single-point, multi-point and information distortion fault location, and has a higher accuracy and convergence rate than other optimization algorithms in solving the distribution network fault location problem. This work is supported by the National Natural Science Foundation of China (No. 51179074).