Research on operation fault diagnosis algorithm of power grid equipment based on power big data

被引：2

作者：

Qian, Jianguo ^{[1
]}

Zhu, Bingquan ^{[1
]}

Li, Ying ^{[1
]}

Shi, Zhengchai ^{[2
]}

机构：

[1] State Grid Zhejiang Elect Power Co, Equipment Monitoring Dept, Hangzhou, Peoples R China

[2] State Grid Zhejiang Elect Power Co, Wenzhou Power Supply Co, Hangzhou, Peoples R China

来源：

ARCHIVES OF ELECTRICAL ENGINEERING | 2020年 / 69卷 / 04期

关键词：

association rules; big data; data mining; fault diagnosis; grid equipment;

D O I：

10.24425/aee.2020.134630

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Power big data contains a lot of information related to equipment fault. The analysis and processing of power big data can realize fault diagnosis. This study mainly analyzed the application of association rules in power big data processing. Firstly, the association rules and the Apriori algorithm were introduced. Then, aiming at the shortage of the Apriori algorithm, an IM-Apriori algorithm was designed, and a simulation experiment was carried out. The results showed that the IM-Apriori algorithm had a significant advantage over the Apriori algorithm in the running time. When the number of transactions was 100 000, the running of the IM-Apriori algorithm was 38.42% faster than that of the Apriori algorithm. The IM-Apriori algorithm was little affected by the value of supportmin. Compared with the Extreme Learning Machine (ELM), the IM-Apriori algorithm had better accuracy. The experimental results show the effectiveness of the IM-Apriori algorithm in fault diagnosis, and it can be further promoted and applied in power grid equipment.

引用

页码：793 / 800

页数：8

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