Open AccessPower Environment Warning Prediction Model Based on Big Data Association Rules
Author(s) -
Dongfang Zhang,
Li Yu,
Ou Wang,
Ning Liang
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/750/1/012127
Subject(s) - troubleshooting , association rule learning , alarm , data mining , computer science , apriori algorithm , fault (geology) , power (physics) , reliability engineering , big data , real time computing , a priori and a posteriori , engineering , philosophy , physics , epistemology , quantum mechanics , seismology , geology , aerospace engineering
With the rapid development of power communication network, power production and operation put forward higher requirements for the stability of power communication network. The power environment system can collect the operation status of communication power supply, equipment and other related alarm information as the basis for troubleshooting. However, due to the failure of a device, there will be some columns of linkage alarm, which increases the difficulty of fault location. In order to solve the above problems, this paper uses the method of big data association rules to carry out data deep mining, and makes in-depth analysis on the location and prediction of power environment fault alarms. Through the correlation between power environment alarms, this paper puts forward the analysis and prediction method of power environment alarms, and realizes the prediction of power environment alarms through the analysis of big data association rules. Based on the in-depth analysis of the Apriori algorithm, this paper designed an improved Apriori algorithm which is suitable for power environment alarm prediction, and greatly improves the efficiency of the algorithm.