基于5G通信时延的配电网馈线自动化切换方法  

Switching method for distribution network feeder automation system based on 5G communication delay

作　　者：朱卫卫 朱清 高文森 刘财华 王录泽 刘增稷 ZHU Weiwei;ZHU Qing;GAO Wensen;LIU Caihua;WANG Luze;LIU Zengji(State Grid Xinjiang Electric Power Company Limited,Urumqi 830063,China;NARI Technology Company Limited,Nanjing 211106,China;College of Automation&College of Artificial Intelligence,Nanjing University of Posts and Telecommunications,Nanjing 210023,China)

机构地区：[1]国网新疆电力有限公司,乌鲁木齐830063 [2]国电南瑞科技股份有限公司,南京211106 [3]南京邮电大学自动化学院人工智能学院,南京210023

出　　处：《综合智慧能源》2024年第5期1-11,共11页Integrated Intelligent Energy

基　　金：国家自然科学基金项目(62073173)。

摘　　要：针对5G通信不确定时延导致数据传输时间难以预测,从而影响馈线自动化(FA)系统故障响应及时性和决策准确性的问题,提出一种基于5G通信时延的配电网FA切换方法。首先,建立馈线终端之间的拓扑关系,根据FA系统中每一分支的最大通信时延计算得到FA系统的实时通信时延;其次,针对不同时延下不同FA策略故障处理速度的历史数据,通过层堆叠长短时记忆神经网络(LSTM)模型进行训练,学习出不同通信时延下故障处理速度最快的FA策略;最后,根据层堆叠LSTM模型的学习结果,选择切换到当前通信时延下故障处理速度最快的FA策略。试验结果表明:该方法能有效应对5G通信的不确定性时延对FA系统的影响,保障FA系统可靠运行;此外,与其他机器学习方法相比,层堆叠LSTM模型在预测准确性和预测时延方面具有优势,能够有效提高馈线终端系统的自适应能力和故障响应速度。Since data transmission time is difficult to predict due to the uncertain delay of 5G communication,the fault response timeliness and decision-making accuracy of a feeder automation(FA)system are affected.Thus,a distribution network FA switching method based on 5G communication delay is proposed.Initially,the topological relationship between feeder terminals is established,and the real-time communication delay of the FA system is calculated based on the maximum communication delay in each branch of the FA system.Subsequently,a stacked Long Short-Term Memory(LSTM)neural network model is trained by the historical data of fault processing time under different FA strategies and various delays,to obtain the FA strategies with the fastest fault handling speed under different communication delays.Finally,based on the learning outcomes of the layer-stacked LSTMmodel,the FA strategy with the shortest fault handling time under a certain communication delay is selected.Experimental results demonstrate that the proposed method effectively mitigates the impact of uncertain delays in 5G communication on FA systems,ensuring their reliable operation.Moreover,compared to other machine learning methods,the layer-stacked LSTMmodel shows advantages in prediction accuracy and prediction delay,effectively enhancing the adaptive capacity and fault response speed of feeder terminals.

关 键 词：馈线自动化 5G通信 通信时延 层堆叠LSTM 故障处理 机器学习 智能配电网 

分 类 号：TM76[电气工程—电力系统及自动化] TP39[自动化与计算机技术—计算机应用技术]