期刊名称：IOP Conference Series: Earth and Environmental Science
印刷版ISSN：1755-1307
电子版ISSN：1755-1315
出版年度：2018
卷号：108
期号：5
页码：052026
DOI：10.1088/1755-1315/108/5/052026
语种：English
出版社：IOP Publishing
摘要：With the power supply level of urban power grid toward high reliability development, it is necessary to adopt appropriate methods for comprehensive evaluation of existing equipment. Considering the wide and multi-dimensional power system data, the method of large data mining is used to explore the potential law and value of power system equipment. Based on the monitoring data of main transformer and the records of defects and faults, this paper integrates the data of power grid equipment environment. Apriori is used as an association identification algorithm to extract the frequent correlation factors of the main transformer, and the potential dependence of the big data is analyzed by the support and confidence. Then, the integrated data is analyzed by PCA, and the integrated quantitative scoring model is constructed. It is proved to be effective by using the test set to validate the evaluation algorithm and scheme. This paper provides a new idea for data fusion of smart grid, and provides a reference for further evaluation of big data of power grid equipment.
