基于蜂群深度神经网络的小电流接地故障选线方法（英文）

Fault line selection in small current ground power system based on Bee-deep neural network

小电流接地故障选线由于选线精度较低和故障选线时间较长的问题,直接影响配电网的稳定运行,针对此问题,本文采用大数据智能分析算法,利用小电流接地故障的历史数据,采用深度卷积网络的结构,应用统计特性,对网络模型和参数进行训练;利用启发式优化算法加速了算法的收敛性,从而降低了选线时间;根据搭建的智能网络模型以及陕西某配电系统的实际测量历史数据得到的小电流接地选线系统能够快速准确的成功选线,从而增强了系统实际应用的效果。

The small current grounding fault line selection due to the low accuracy of line selection and fault line selection problem for a longer time, directly affects the stable operation of the power distribution network, aiming at this problem, in this paper, using big data intelligent analysis algorithm, using the historical data of the small current grounding fault, the depth of convolution network structure, the statistical properties of the application of network model and parameters for training;Heuristic optimization algorithm is used to accelerate the convergence of the algorithm and reduce the line selection time. According to the established intelligent network model and the actual measurement history data of a distribution system in Shanxi province, the small current grounding line selection system can quickly and accurately select the line successfully, thus enhancing the effect of practical application of the system.