基于支持度-置信度-提升度的配网自动化系统数据挖掘算法及应用*#
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摘要
受到通信或其它设备及配网二次回路故障、缺陷及运行单位人为修改的影响,本已庞大、复杂的配网自动化控制系统数据库的数据质量问题更为严重,数据不准确、不一致、不可靠等问题层出不穷。针对这一问题,文章设计了一种基于支持度-置信度-提升度框架的挖掘算法,从配网自动化控制系统历史数据库中低质量的海量数据中智能挖掘频繁项集,建立符合配网自动化动作逻辑的、具备整体一致性的强关联、强相关规则。文章以具体案例详细介绍了该算法的应用,并通过分析说明该算法不仅为建立客观、合理的配网自动化系统指标评价体系提供可靠、准确的数据挖掘方法以及科学的理论依据,而且可以为管理部门日常运行、管理、维护、消缺工作提供智能的判定工具,节约人力、物力及时间成本,具有很强的工程意义。
Affected by the faults,defects of communication equipments and secondary circuits,and artificial records made by operating staff etc.,the quality of the mass and complicated datum stored in the database of automation operation system of distribution network becomes poorer and more worse,incorrect,un-uniform,unreliability and so on. Aiming at this problem,a data-mining algorithm based on the support-confidence-lift framework is designed to mine frequent item sets,strong association and correlation rules that conform to the logic of the automation operation system from the massive poor and complicated datum of the automation operation system of distribution networks. In addition,a case on a practical application of the algorithm is studied carefully to illustrate that the designed algorithm can not only offer an exact,reliable data mining method,a theoretical foundation for setting up an objective and reasonable utility indices system,but also provide intelligent judging tools to routine operation,maintenance,defect elimination and management for the management departments of distribution networks,with saving a lot of labor,physical resources and time cost. Therefore,the algorithm is valuable to the engineering practices of distribution network.
Affected by the faults,defects of communication equipments and secondary circuits,and artificial records made by operating staff etc.,the quality of the mass and complicated datum stored in the database of automation operation system of distribution network becomes poorer and more worse,incorrect,un-uniform,unreliability and so on. Aiming at this problem,a data-mining algorithm based on the support-confidence-lift framework is designed to mine frequent item sets,strong association and correlation rules that conform to the logic of the automation operation system from the massive poor and complicated datum of the automation operation system of distribution networks. In addition,a case on a practical application of the algorithm is studied carefully to illustrate that the designed algorithm can not only offer an exact,reliable data mining method,a theoretical foundation for setting up an objective and reasonable utility indices system,but also provide intelligent judging tools to routine operation,maintenance,defect elimination and management for the management departments of distribution networks,with saving a lot of labor,physical resources and time cost. Therefore,the algorithm is valuable to the engineering practices of distribution network.
引文
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[2]赵腾,张焰,张东霞.智能配电网大数据应用技术与前景分析[J].电网技术,2014,38(12):3305-3312.Zhao Teng,Zhang Yan,Zhang Dongxia. Application technology of big data in smart distribution grid and its prospect analysis[J]. Power System Technology,2014,2014,38(12):3305-3312.
[3]刘道新,胡航海,张健,等.大数据全生命周期中关键问题研究及应用[J].中国电机工程学报,2015,35(1):23-28.Liu Dao Xin,Hu Haihang,Zhang Jian,et al. Research on key issues of big data lifecycle and its applications[J] Proceedings of the CSEE,2015,35(1):23-28.
[4]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(1):2-12.Zhang Dongxia,Miao Xin,Liu Li Ping,et al. Research on Development Strategy for Smart Grid Big Data[J]Proceedings of the CSEE,2015,35(1):2-12.
[5]宋亚奇,周国亮,朱永利.智能电网大数据处理技术现状与挑战[J].电网技术,2013,37(4):927-935.Song Yaqi,Zhou Guoliang,Zhu Yongli. Present Status and Challenges of Big Data Processing in Smart Grid[J]. Power System Technology,2013,37(4):927-935.
[6] M. Kezunovic,L. Xie,S. Grijalva. The role of big data in improving power system operation and protection[C]. Bulk Power System Dynamics&Control-ix Optimization,Security&Control of the Emerging Power Grid,Irep Symposium,2013:1-9.
[7] B. Wang,BW. Fang,YJ. Wang,et al. Power system transient stability assessment based on big data and the core vector machine[J]. ieee transactions on smart grid,2016,7(5):2561-2570.
[8] YJ. Yan,R CM. Qiu,XC. Jiang. Big Data Modeling and Analysis for Power Transmission Equipment:A Novel Random Matrix Theoretical Approach[J]. IEEE Access,2018,6:7148-7156.
[9]王璟,杨德昌,李锰,等.配电网大数据技术分析与典型应用案例[J].电网技术,2015,39(11):3114-3121.Wang Jing,Yang Dechang,Li Meng,et al. Analysis of big data technology in power distribution system and typical applications[J],2015,39(11):3114-3121.
[10]曲朝阳.智能电网知识处理模型与可视化方法[M].科学出版社,2013:1-50.Qu Chaoyang. Smart power grid knowledge processing model and visualization method[M]. First Edition,Science Press,2013:1-50.
[11]刘巍,黄曌,李鹏,等.面向智能配电网的大数据统一支撑平台体系与构架[J].电工技术学报,2014,29(s1):486-491.Liu Wei,Huang Zhao,Li Peng,et al. Summary about System and Framework of Unified Supporting Platform of Big Data for Smart Distribution Grid[J] Transactions of China Electrotechnical Society,2014,29(s1):486-491.
[12]彭小圣,邓迪元,程时杰,等.面向智能电网应用的电力大数据关键技术[J].中国电机工程学报,2015,35(3):503-511.Peng Xiaosheng,Deng Diyuan,Cheng Shijie,et al. Key technologies of electric power big data and its application prospects in smart grid[J]Proceedings of the CSEE,2015,35(1):503-511.
[13]NP. Yu.,R. Johnson,R. Sherick,et al. Big data analytics in power distribution systems[C]. Innovative Smart Grid Technologies Conference,2015.
[14]Y. Shi. Dynamic data mining on multi-dimensional data[J]. Dissertation Abstracts International,Volume:67-04,Section:B,page:2087.;Adviser:Zhang AD.,2006:220--229.
[15]JW. Han,M,PEI J. KAMBER. Data mining concepts and techniques[M]. Second Edition,Morgan Kaufmann,Elsevier,San Francisco,2006:243-278.