Open AccessResearch on Fault-Environment Association Rules of Distribution Network Based on Improved Apriori Algorithm
Author(s) -
Maoran Xiao,
Yuanyuan Sun,
Kejun Li
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/569/3/032079
Subject(s) - randomness , apriori algorithm , data mining , reliability (semiconductor) , computer science , association rule learning , warning system , fault (geology) , algorithm , distribution (mathematics) , key (lock) , a priori and a posteriori , causality (physics) , power (physics) , mathematics , statistics , computer security , telecommunications , mathematical analysis , physics , philosophy , epistemology , quantum mechanics , seismology , geology
As the urban power grids gradually enter the high reliability level, the distribution network risk early warning becomes the key to further improve the reliability level. Distribution network faults have the characteristics of strong randomness and weak causality, and conventional methods are difficult to find their laws. The idea of data mining is introduced in this paper. Based on the analysis of various types of fault data, the improved Apriori algorithm is used to mine the strong correlation rules of various influencing factors in the distribution network, and the fault-environment pattern recognition library of distribution network is established to lay the foundation for the early warning of distribution network operation risk.