Mining Method of Voltage Sag Association Rules Based on Multi-sources Monitoring Data

被引:1
作者
Peng, Heping [1 ]
Luan, Le [1 ]
Xu, Zhong [1 ]
Mo, Wenxiong [1 ]
Wang, Yong [1 ]
机构
[1] State Grid Guangdong Elect Power Co Ltd, Guangzhou Power Supply Co, Guangzhou, Peoples R China
来源
2021 IEEE IAS INDUSTRIAL AND COMMERCIAL POWER SYSTEM ASIA (IEEE I&CPS ASIA 2021) | 2021年
关键词
lighting; voltage sag; AprioriTid algorithm; multi-platform; K-means algorithm; QUALITY;
D O I
10.1109/ICPSAsia52756.2021.9621662
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
With the development of digital technology, multi-platform monitoring data have changed from processed objects into basic resources. This paper summarizes the rules of voltage sag events caused by lightning and mines the association rules with multi-platform data. These rules improve the safety and reliability of the distribution network. However, there is a lack of a method to mine the association rules with lightning and voltage sags. By using data of lightning location system and power quality monitoring system, an association rule mining method, which consists of two main steps, is proposed: 1) establish a database based on multi-platform and build a decision table by discrete attributes, 2) propose the improved AprioriTid algorithm considering nonuniform data to mine rules. A comparative study of multi-platform monitoring data is carried out on the use of the traditional and improved AprioriTid algorithms. And the accuracy of the proposed algorithm is validated by measured data.
引用
收藏
页码:1551 / 1555
页数:5
相关论文
共 17 条
  • [1] k-means based load estimation of domestic smart meter measurements
    Al-Wakeel, Ali
    Wu, Jianzhong
    Jenkins, Nick
    [J]. APPLIED ENERGY, 2017, 194 : 333 - 342
  • [2] A new discretization algorithm based on range coefficient of dispersion and skewness for neural networks classifier
    Augasta, M. Gethsiyal
    Kathirvalavakumar, T.
    [J]. APPLIED SOFT COMPUTING, 2012, 12 (02) : 619 - 625
  • [3] Deshpande R, 2019, INT J RENEW ENERGY R, V9, P281
  • [4] Hammerstein System Identification With the Nearest Neighbor Algorithm
    Greblicki, Wlodzimierz
    Pawlak, Miroslaw
    [J]. IEEE TRANSACTIONS ON INFORMATION THEORY, 2017, 63 (08) : 4746 - 4757
  • [5] Application of cluster analysis to identification flagged power quality measurements in area-related approach
    Jasinski, Michal
    Sikorski, Tomasz
    Borkoswski, Klaudiusz
    [J]. PRZEGLAD ELEKTROTECHNICZNY, 2020, 96 (03): : 9 - 12
  • [6] Kim J., 2013, EXPERT SYST APPL, V12, P157
  • [7] A clustering algorithm with affine space-based boundary detection
    Li, Xiangli
    Han, Qiong
    Qiu, Baozhi
    [J]. APPLIED INTELLIGENCE, 2018, 48 (02) : 432 - 444
  • [8] Optimization of Transmission-Line Route Based on Lightning Incidence Reported by the Lightning Location System
    Li, Yongfu
    Yang, Qing
    Sima, Wenxia
    Li, Jiaqi
    Yuan, Tao
    [J]. IEEE TRANSACTIONS ON POWER DELIVERY, 2013, 28 (03) : 1460 - 1468
  • [9] Data mining technology for mechanical engineering computer test system
    Li, Zhenjun
    Yu, Xiaomo
    [J]. MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2020, 141
  • [10] Quality of the supplied electric service: A tool to evaluate the need of protection against lightning surges
    Marzinotto, M.
    Fiamingo, F.
    Mazzetti, C.
    Lo Piparo, G. B.
    [J]. ELECTRIC POWER SYSTEMS RESEARCH, 2012, 85 : 75 - 81