基于大数据与物联网的输变电设备故障诊断研究-《计算机技术与发展》

文章信息/Info

Title:: Research on Substation Fault Detection and Operation OptimizationBased on Data Mining and IoT

Author(s):: DENG Yan-shan; ZHAO Kai-li; LYU Wen-chao; BAI Jie; DONG Jie; GAO Ling; Tangshan Power Supply Company of State Grid Jibei Electric Power Co. ,Ltd. ,Tangshan 063000,China

Keywords:: transmission and transformation equipment; grid faults; big data; internet of things technology; traveling wave positioningmethod; BP neural network algorithm model

摘要:: 针对输变电设备运行过程中各种因素导致的电网故障,提出了新型的故障诊断方法。该方法以大数据技术和物联网技术为基础,构建出包含数据采集层、数据传输层、数据分析层和数据监控层的物联网检测系统,实现了底层设备的物联网信息通讯。在故障诊断过程中,还融合了行波定位方法和 BP 神经网络算法模型,通过波定位方法能够使用户实时获取输变电设备中不同监测节点的暂态电压、暂态电流数据,进而迅速、精确地定位出输变电设备故障发生位置。通过 BP神经网络算法模型对行波定位后的故障信息进行进一步地优化,对定位到的输变电设备故障位置信息进行更精确地学习和训练,以提高输变电设备的故障数据处理能力。试验表明,该方案误差率较低,大大提高了输变电设备故障诊断能力,从而提高了系统整体运行的效率。

Abstract:: Aiming at the power grid failure caused by various factors during the operation of transmission and transformation equipment,a new fault diagnosis method is proposed. Based on the big data technology and the Internet of Things technology,this method builds an IoT detection system including data acquisition layer,data transmission layer,data analysis layer and data monitoring layer,and realizes the IoT information communication of the underlying equipment. In the process of fault diagnosis,the traveling wave positioning method and BP neural network algorithm model are also integrated. Through the wave positioning method, users can obtain the real - time transient voltage and transient current data of different monitoring nodes in power transmission and transformation equipment,and the nquickly,accurately locate the fault occurrence location of transmission and transformation equipment. The BP neural network algorithm model is used to further optimize the fault information after traveling wave localization,and more accurately learn and train the fault location information of the located transmission and transformation equipment to improve the fault data processing capability of the trans鄄mission and transformation equipment. Experiment shows that the error rate of the proposed scheme is low,which greatly improves the fault diagnosis capability of transmission and transformation equipment, there by improving the overall system operation efficiency.