Intelligent Processing of Power Operation Data Based on Improved Apriori Algorithm

被引：0
|
作者：
Zhao, Xin [1 ]
Huang, Changda [1 ]
机构：
[1] State Grid Xinjiang Electric Power Co., Ltd Marketing Service Center, Qin Yang,454550, China
来源：
Strategic Planning for Energy and the Environment | 2024年 / 43卷 / 02期
关键词：
This paper addresses several problems in the power system. Key challenges include low-power information integration; inappropriate system data management; inaccurate system data updating; and inefficient fault diagnosis. We focus on analyzing and diagnosing transmission line faults using the operation data of the power system. The study incorporates the quantitative identification of statements. This is done using the Apriori big data analysis and calculation method. Additionally; we utilize big data analysis and vast power operation data. We aim to achieve automatic analysis and pinpoint the causes of transmission line faults. Furthermore; we seek to optimize the traditional Apriori calculation method. This optimization results in a reduction of about 52% in the candidate item set calculation. The optimized M-Apriori calculation method can analyze the correlation between event index data and faults in real time; and realize automatic diagnosis and analysis of faults through operation data. © 2024 River Publishers;
D O I：
10.13052/spee1048-5236.43213
中图分类号：
学科分类号：
摘要：
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页码：477 / 498
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