Intelligent Processing of Power Operation Data Based on Improved Apriori Algorithm

Q3 Environmental Science
Xin Zhao, Changda Huang
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引用次数: 0

Abstract

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.
基于改进的 Apriori 算法的电力运行数据智能处理技术
本文探讨了电力系统中的几个问题。主要挑战包括低功耗信息集成、不恰当的系统数据管理、不准确的系统数据更新以及低效的故障诊断。我们的重点是利用电力系统的运行数据分析和诊断输电线路故障。该研究结合了语句的定量识别。这是利用 Apriori 大数据分析和计算方法完成的。此外,我们还利用了大数据分析和庞大的电力运行数据。我们的目标是实现自动分析,准确定位输电线路故障的原因。此外,我们还力求优化传统的 Apriori 计算方法。优化后,候选项集计算量减少了约 52%。优化后的 M-Apriori 计算方法可实时分析事件指标数据与故障之间的相关性,并通过运行数据实现故障的自动诊断和分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Strategic Planning for Energy and the Environment
Strategic Planning for Energy and the Environment Environmental Science-Environmental Science (all)
CiteScore
1.50
自引率
0.00%
发文量
25
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