基于日志信息的电网离散事件动态模型学习和分析  

Learning and Analysis of Dynamic Models for Grid Discrete Events Based on Log Information

作　　者：朱丹龙 闫云琦 陈颖[2] 张家琦 晋龙兴 付威 Zhu Danlong;Yan Yunqi;Chen Ying;Zhang Jiaqi;Jin Longxing;Fu Wei(Shenzhen Power Grid Utility Co.LTD,Shenzhen 518048,China;Department of Electrical Engineering,Tsinghua University,Beijing 100084,China)

机构地区：[1]深圳供电局有限公司,广东深圳518048 [2]清华大学电机系,北京100084

出　　处：《系统仿真学报》2023年第10期2193-2201,共9页Journal of System Simulation

基　　金：国家自然科学基金企业创新发展联合基金重点资助项目(U22B2096)。

摘　　要：随着电网规模的增加,电网设备产生的海量日志信息为人工分析电网异常状态带来了挑战。电网运行中的日志信息具有典型的离散序列特征,通过分析电网告警信息日志,提出了厂站事件转移概率模型和事件序列风险计算方法,可以有效建模和分析厂站一二次系统运行异常水平。该方法不仅可以成功地识别出已被调度员人工标记的电网故障对应事件序列,还能识别出未曾被调度员记录的异常告警信息序列,有助于分析研判电网设备运行态势,发现潜在风险,提升变电站运维效率。With the increasing scale of power grid,the massive amount of log information generated by devices in the power grid poses a challenge to the manual analysis of abnormal grid conditions.The log information generated during the operation of the power grid has the typical discrete sequential characteristics.By analyzing the log information of grid alarm messages,a station event transition probability model and an event sequence risk calculation method are proposed to effectively model and analyze the abnormal operation level of primary and secondary systems in substations.The proposed method not only successfully identifies the event sequences corresponding to grid failures that have been manually recarded by dispatchers but also identifies the abnormal alarm information sequences that have not been recorded by dispatchers.This helps analyze and assess the operational situation of power grid equipment,discover potential risks,and improve the efficiency of substation maintenance.

关 键 词：电力系统 告警信息 动态特征学习 异常识别 风险评估 

分 类 号：TM769[电气工程—电力系统及自动化] TP391[自动化与计算机技术—计算机应用技术]