Title :
A distribution network fault data analysis method based on association rule mining
Author :
Gao Zhanjun ; Peng Zhengliang ; Gao Nuo ; Chen Bin
Author_Institution :
Key Lab. of Power Syst. Intell. Dispatch & Control, Minist. of Educ., Shandong Univ., Jinan, China
Abstract :
This paper presents a new method of the distribution network fault diagnose based on data mining. This method uses the fault information produced in the process of distribution network fault handling to study the spatial and temporal characteristics of distribution network fault. It uses APRIORI algorithm to establish database of fault attributes and mine the association rules of fault information. Then the distribution network fault is diagnosed by using the strong association rules database without real time monitoring information supplied by distribution automation system. The reliability of power supply will be improved greatly through this method.
Keywords :
data mining; fault diagnosis; power distribution faults; power distribution reliability; power engineering computing; power supply quality; spatiotemporal phenomena; APRIORI algorithm; association rule data mining; distribution automation system; distribution network fault diagnosis data analysis method; fault handling; power supply reliability; spatial and temporal characteristics; Association rules; Fault diagnosis; Itemsets; Power systems; APRIORI algorithm; Association rules mining; Distribution network; Fault diagnosis;
Conference_Titel :
Power and Energy Engineering Conference (APPEEC), 2014 IEEE PES Asia-Pacific
DOI :
10.1109/APPEEC.2014.7066121
