Association analysis for defect data of secondary device in smart substations based on improved Apriori algorithm

被引：0
|
作者：
Chen Y. [1 ]
Li S. [1 ]
Zhang L. [1 ]
Lu H. [2 ]
Dai Z. [2 ]
机构：
[1] Electric Power Research Institute of Yunnan Power Grid Co., Ltd., Kunming
[2] North China Electric Power University (Baoding), Baoding
来源：
Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control | 2019年 / 47卷 / 20期
基金：
中国国家自然科学基金;
关键词：
Apriori algorithm; Association analysis; Defect; Secondary device; Smart substation;
D O I：
10.19783/j.cnki.pspc.181390
中图分类号：
学科分类号：
摘要：
The development of smart substations provide technical support for collection and management of big data, and abundant defect data for related analysis about the secondary device. In view of this, this paper establishes the defect data model of the secondary device in smart substations firstly. Secondly, according to characteristics of the defect data model, the Apriori algorithm is improved to reduce the time complexity and memory occupation. Finally, taking defect data of the secondary device of a city in one year as an example, the association rules of the defect data are acquired and analyzed by the improved Apriori algorithm. The result shows that the proposed method can analyze the device defect, search for the weakness of the secondary device, and provide the support for the formulation of defect inspection modes. Besides, compared with the traditional Apriori algorithm, the improved Apriori algorithm reduces the time complexity. © 2019, Power System Protection and Control Press. All right reserved.
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页码：135 / 141
页数：6
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