基于数据挖掘的变电站告警数据分析和巡检策略研究
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摘要
鉴于目前高压变电站告警数据多、运维人员巡检效率低的情况,提出了一种基于关联规则的告警数据分析方法。通过关联规则得到大量告警事件间隐含的先导—后继关系,分析故障发生后运行人员工作站记录的告警报文,这有助于运维人员找出故障的根本原因;并参考相关的电力设备状态量评定标准,综合告警事件间的关系与各巡检单元权重系数,确定系统频繁告警后巡检的优先顺序,以提高巡检效率。最后,通过西北电网某变电站主变近一年的告警数据来具体说明这种方法,研究发现该站主变存在着由于冷却器就地操作、直流控制电源故障引起的频繁冷却器油流停止告警,需要重点巡视冷却装置控制系统;并提出了改进的巡检策略来提高巡检效率。该方法为变电站实施新的巡检策略提供理论支撑。
Considering that there are many alarm data in high voltage substation at present and the inspection efficiency of operation and maintenance personnel is low,this paper presents a method based on association rules for alarm data analysis. After identifying the pilotsuccessive relationship covered in the numerous alarm data through the association rules,and alarm messages recorded by operator 's workstations after the failure are analyzed,which is helpful for the operation and maintenance personnel to find out the root cause of the failure. Meanwhile,referring to the interrelated equipment status assessment standards,alarm events relationships and weights of each inspection unit determine the priority of the inspection after OWS frequently alarms,which contributes to improve inspection efficiency. Finally,this method is illustrated through the alarm data of a station transformer of northwest power grid for nearly one year,and the reasons that the transformer frequently alarm are raised. It is found that the main transformer of the station has frequent oil flow stop alarm caused by the local operation of the cooler and the fault of DC control power supply. It is necessary to focus on inspecting the control system of the cooling device. An improved inspection strategy is put forward to improve the inspection efficiency. This method provides theoretical support for the implementation of new inspection strategy in substations.
Considering that there are many alarm data in high voltage substation at present and the inspection efficiency of operation and maintenance personnel is low,this paper presents a method based on association rules for alarm data analysis. After identifying the pilotsuccessive relationship covered in the numerous alarm data through the association rules,and alarm messages recorded by operator 's workstations after the failure are analyzed,which is helpful for the operation and maintenance personnel to find out the root cause of the failure. Meanwhile,referring to the interrelated equipment status assessment standards,alarm events relationships and weights of each inspection unit determine the priority of the inspection after OWS frequently alarms,which contributes to improve inspection efficiency. Finally,this method is illustrated through the alarm data of a station transformer of northwest power grid for nearly one year,and the reasons that the transformer frequently alarm are raised. It is found that the main transformer of the station has frequent oil flow stop alarm caused by the local operation of the cooler and the fault of DC control power supply. It is necessary to focus on inspecting the control system of the cooling device. An improved inspection strategy is put forward to improve the inspection efficiency. This method provides theoretical support for the implementation of new inspection strategy in substations.
引文
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[20]国家能源局.DL272-2012,220 k V~750 k V油浸式变压器使用技术条件[S].北京:中国电力出版社,2012.
[2]费思源.大数据技术在配电网中的应用综述[J].中国电机工程学报,2018,38(01):85-96+345.FEI Siyuan.Overview of application of big dat-a technology in power distribution system[J].Proceedings of the CSEE,2018,38(01):85-96+345.
[3]张东霞,苗新,刘丽平,等.智能电网大数据技术发展研究[J].中国电机工程学报,2015,35(01):2-12.ZHANG Dongxia,MIAO Xin,LIU Liping,et al.Research on development strategy for smart grid big data[J].Proceedings of the CSEE,2015,35(01):2-12.
[4]黄绪勇,孙鹏,耿苏杰,等.基于关联规则和变权重系数的SF6高压断路器状态综合评估[J].电力系统保护与控制,2018,46(02):50-56.HUANG Xuyong,SUN Peng,GENG Sujie,et al.An assessment method of SF6high-voltage circuit breaker based on association rules and variable weight coefficients[J].Power System Protection and Control,2018,46(02):50-56.
[5]谢荣斌,张登,林福昌,等.基于关联规则与变权重的变压器状态评估方法[J].高压电器,2014,50(01):133-138.XIE Rongbin,ZHANG Deng,LIN Fuchang,et al.Transformer condition assessment using association rules and variable weight[J].High Voltage A-pparatus,2014,50(01):133-138.
[6]王珣,王馨,赵盟,等.输变电设备状态大数据分析应用探讨[J].电力大数据,2018,21(01):1-5.WANG Xun,WANG Xin,ZHAO Meng,et al.Discussion on big data analysis and application of power transmission and transformation equipment[J].Power Systems and Big Data,2018,21(01):1-5.
[7]贾树森,董祥宁,王安山,等.基于大数据统计分析的变电站巡视质量控制策略[J].电力大数据,2018,21(11):63-67.JIA Shusen,DONG Xiangning,WANG Anshan,et al.The strategy of substation patrol quality control based on the big data statistical analysis[J].Power Systems and Big Data,2018,21(11):63-67.
[8]L.CAO,Y.ZHAO,H.ZHANG,et al.Flexible frameworks for actionable knowledge discovery[J].IEEE Transactions on Knowledge and Data Engineering,2010,22(9)1299-1312.
[9]王磊,陈青,高洪雨,等.基于大数据挖掘技术的智能变电站故障追踪架构[J].电力系统自动化,2018,42(03):84-91.WANG Lei,CEHN Qing,GAO Hongyu,et al.Framework of fault trace for smart substation base-d on big data mining technology[J].Automation of Electric Power Systems,2018,42(03):84-91.
[10]梁志成,彭建,吴碧海,等.远程的变电站自动化智能数据挖掘系统[J].广东电力,2014,27(03):73-79.LIANG Zhicheng,PENG Jian,WU Bihai,et al.Remote automation intelligent data mining syste-m of substation[J].Guangdong Electric Power,2014,27(03):73-79.
[11]T DIMITROVSKA,U RUDEZ,R MIHALIC.Indirect powersystem contingency screening for real-time applications based on PCA[J].IEEE Transactions on Power Systems,2018,33(01):1080-1081.
[12]沈海平,姚楠,黄薛凌.基于智能视觉的变电站设备状态智能巡视[J].现代电子技术,2017,40(09):169-172.SHEN Haiping,YAO Nan,HUANG Xueling.Substation equipment state intelligent patrol based on intelligent vision[J].Modern Electronics Technique,2017,40(09):169-172.
[13]严肃.红外测温技术在变电站设备巡视中的运用分析[J].通信电源技术,2018,35(06):176-177.YAN Su.Application analysis of infrared temperature measurement technology in substation equipment inspection[J].Telecom Power Technology,2018,35(06):176-177.
[14]张飞飞,樊锐轶,孙广辉,等.大数据环境下变电站智能监控方案研究[J].智慧电力,2016,44(06):72-76.ZHANG Feifei,FAN Ruitie,SUN Guanghui,et al.Study on station intelligent monitoring scheme under big data environment[J].Smart Power,2016,44(06):72-76.
[15]叶杰宏,芦兴.变电设备差异化运维策略的研究与应用[J].电力与能源,2015,36(06):775-778.YE Jiehong,LU Xing.Research and application of differential operational strategy for substation equipment[J].Power&Energy,2015,36(06):775-778.
[16]石磊,王剑宏,徐明,等.500 k V无人值守变电站巡视质量的智能优化[J].浙江电力,2014,33(04):26-28+33.SHI Lei,WANG Jianhong,XU Ming,et al.Intelligent optimization on inspection quality of 500 kV unattended substations[J].Zhejiang Electric Power,2014,33(04):26-28+33.
[17]姜玉宏,庞曼,耿东,等.变电设备差异化巡视优化[J].黑龙江电力,2017,39(01):78-80+91.JIANG Yuhong,PANG Man,GENG Dong,et al.Optimization of differentiating inspection of sub-station equipment[J].Heilongjiang Electric Pow-er,2017,39(01):78-80+91.
[18]吴素我,张焰,苏运.基于配用电数据关联的中压配电网断线故障诊断方法[J].电力自动化设备,2017,37(07):101-109.WU Suwo,ZHANG Yan,SU Yun.Open-line fault diagnosis based on data association of MV distribution network[J].Electric Power Automation Equipment,2017,37(07):101-109.
[19]WENXIN GUO,FUSHUAN WEN,ZHIWEI LIAO,et al.An analytic model-based Approach for power system alarm processing employing temporal constraint network[J].IEEE Transctions on Power Delivery,2010,25(04):2435-2447.
[20]国家能源局.DL272-2012,220 k V~750 k V油浸式变压器使用技术条件[S].北京:中国电力出版社,2012.