输变电设备集中监控大数据研究及应用
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摘要
随着智能电网的快速发展和调控一体化运行体系的深入推进,接入调控中心的输变电设备集中监控信息数据成倍增长,如何从海量的监控信息数据中挖掘出需要的信息,直接指导监控员的工作,由被动监控转为主动监控,显得尤为迫切。国网浙江省电力有限公司针对现状率先开展了输变电设备集中监控信息大数据技术的研究与应用,基于智能电网调控技术支持系统搭建了一个由各类监控信息分析模块组成的信息分析中心,通过对输变电设备告警信息进行挖掘分析实现集中监控信息的优化整合和分类处置,可以显著提升监控员的日常工作效率。文章首先概述了输变电设备集中监控信息分析中心的总体结构,然后介绍了其采用的优化算法,最后结合浙江电网的实际运行情况,对各类监控信息分析模块进行了具体应用,其分析结果可为监控员提供相应的辅助决策,为浙江电网的安全运行提供保障。
With the rapid development of the smart grid and further promotion of the control integration operating system,the centralized monitoring information data of electric transmission and transformation equipment connected to the control center has presented the exponential growth. It becomes extremely urgent to dig for information needed from the massive monitoring information data,guide the work of monitors directly and turn the passive monitoring to be active monitoring. State Grid Zhejiang Electric Power Corporation takes the lead in launching the researches and application of the big data of centralized monitoring information of the electric transmission and transformation equipment by aiming at the current status,and establishes an information analysis center comprised by all kinds of monitoring information analysis modules based on the smart grid control technology support system,which will implement the optimized integration and sorted processing of centralized monitoring information by conducting the big data mining analysis through the warning messages of electric transmission and transformation equipment,and improve the daily work efficiency of monitors significantly. In the paper,the overall structure of the centralized monitoring information analytic center of the electric transmission and transformation equipment are introduced generally first. Then the optimization algorithm which is adopted by the system is introduced. Finally,combining the actual operation conditions of Zhejiang power grid,the specific application of various monitoring information analytic modules are implemented,and the analysis result will not only provide corresponding aided decision for the monitors,but also ensure the safe operation of Zhejiang power grid.
With the rapid development of the smart grid and further promotion of the control integration operating system,the centralized monitoring information data of electric transmission and transformation equipment connected to the control center has presented the exponential growth. It becomes extremely urgent to dig for information needed from the massive monitoring information data,guide the work of monitors directly and turn the passive monitoring to be active monitoring. State Grid Zhejiang Electric Power Corporation takes the lead in launching the researches and application of the big data of centralized monitoring information of the electric transmission and transformation equipment by aiming at the current status,and establishes an information analysis center comprised by all kinds of monitoring information analysis modules based on the smart grid control technology support system,which will implement the optimized integration and sorted processing of centralized monitoring information by conducting the big data mining analysis through the warning messages of electric transmission and transformation equipment,and improve the daily work efficiency of monitors significantly. In the paper,the overall structure of the centralized monitoring information analytic center of the electric transmission and transformation equipment are introduced generally first. Then the optimization algorithm which is adopted by the system is introduced. Finally,combining the actual operation conditions of Zhejiang power grid,the specific application of various monitoring information analytic modules are implemented,and the analysis result will not only provide corresponding aided decision for the monitors,but also ensure the safe operation of Zhejiang power grid.
引文
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[15]朱州,吴漾.基于改进K-means聚类算法在电力客户价值分群的应用[J].计算机与数字工程,2017,45(06):1049-1054.ZHU Zhou,WU Yang.Application of improved k-means clustering algorithm in clustering based on power customer value[J].Computer&Digital Engineering,2017,45(06):1049-1054.
[2]左亚芳.电网调度与监控[M].北京:中国电力出版社,2013.
[3]张沛,吴潇雨,和敬涵.大数据技术在主动配电网中的应用综述[J].电力建设,2015,36(01):52-59.ZHANG Pei,WU Xiaoyu,HE Jinghan.Review on big data technology applied in active distribution network[J].Electric Power Construction,2015,36(01):52-59.
[4]杨甲甲,赵俊华,文福拴,等.智能电网环境下基于大数据挖掘的居民负荷设备识别与负荷建模[J].电力建设,2016,37(12):11-23.YANG Jiajia,ZHAO Junhua,WEN Fushuan,et al.Residential appliance identification and load modeling based on big date mining in smart grid environment[J].Electric Power Construction,2016,37(12):11-23.
[5]张根周.大数据在智能电网领域的应用[J].电网与清洁能源,2016,32(06):114-117.ZHANG Genzhou.Applications of big data in the field of smart grid[J].Power System and Clean Energy,2016,32(06):114-117.
[6]鲁华永,袁越,郭泓佐,等.基于正则表达式的变电站集中监控信息解析方法[J].电力系统自动化,2017,41(05):78-83.LU Huayong,YUAN Yue,GUO Hongzuo,et al.Regular expressions based information analytic method for substation centralized monitoring[J].Automation of Electric Power Systems,2017,41(05):78-83.
[7]王伟,蒋菱,王峥,等.基于向量评估遗传算法的智能电网大数据交易模型研究[J].电网与清洁能源,2016,32(10):1-8.WANG Wei,JIANG Ling,WANG Zheng,et al.Trade model of smart grid big data based on vector evaluated genetic algorithm[J].Power System and Clean Energy,2016,32(10):1-8.
[8]吴凯峰,刘万涛,李彦虎,等.基于云计算的电力大数据分析技术与应用[J].中国电力,2015,48(02):111-116.WU Kaifeng,LIU Wantao,LI Yanhu,et al.Cloud-computing based power big data analysis technology and its application[J].Electric Power,2015,48(02):111-116.
[9]王德文,宋亚奇,朱永利.基于云计算的智能电网信息平台[J].电力系统自动化,2010,34(22):7-12.WANG Dewen,SONG Yaqi,ZHU Yongli.Information platform of smart grid based on cloud computing[J].Automation of electric power systems,2010,34(22):7-12.
[10]刘广一,朱文东,陈金祥,等.智能电网大数据的特点、应用场景与分析平台[J].南方电网技术,2016,10(05):102-110.LIU Guangyi,ZHU Wendong,CHEN Jinxiang,et al.Characteristics,Application scenarios and analysis platform of smart grid big data[J].Southern Power System Technology,2016,10(05):102-110.
[11]孙鸿飞,弓丽栋,张海涛,等.智能电网大数据分析框架及其应用演进研究[J].现代电力,2016,33(06):64-73.SUN Hongfei,GONG Lidong,ZHANG Haitao,et al.Research on big data analysis platform for smart grid and its application evolution[J].Modern Electric Power,2016,33(06):64-73.
[12]王钦,蒋怀光,文福拴,等.智能电网中大数据的概念、技术与挑战[J].电力建设,2016,37(12):1-10.WANG Qin,JIANG Huaiguang,WEN Fushuan,et al.Concept,Technology and challengs of big data in smart grid[J].Electric Power Construction,2016,37(12):1-10.
[13]唐升卫,刘菲,黄缙华.面向智能电网的负荷调度控制系统设计及应用[J].广东电力,2015,28(06):47-52.TANG Shengwei,LIU Fei,HUANG Jinhua.Design and application of load dispatching control system for smart grid[J].Guangdong Electric Power,2015,28(06):47-52.
[14]郭晨晨,朱红康.一种基于MapReduce的改进k-means聚类算法研究[J].河北工业大学学报,2016,45(05):35-43.GUO Chenchen,ZHU Hongkang.An improved K-means clustering algorithm based on Mapreduce[J].Journal of Hebei University of Technology,2016,45(05):35-43.
[15]朱州,吴漾.基于改进K-means聚类算法在电力客户价值分群的应用[J].计算机与数字工程,2017,45(06):1049-1054.ZHU Zhou,WU Yang.Application of improved k-means clustering algorithm in clustering based on power customer value[J].Computer&Digital Engineering,2017,45(06):1049-1054.