Research on application of big data mining technology in performance optimization of steam turbines

被引：0

作者：

Wan X. ^{[1
]}

Hu N. ^{[1
]}

Han P. ^{[1
]}

Zhang H. ^{[1
]}

Li S. ^{[1
]}

机构：

[1] School of Power and Mechanical Engineering,, Wuhan University, Wuhan, 430072, Hubei Province

来源：

Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering | 2016年 / 36卷 / 02期

关键词：

Association rules; Big data; MapRreduce; Operation; Performance optimization; Steam turbines; Targeted values;

D O I：

10.13334/j.0258-8013.pcsee.2016.02.017

中图分类号：

学科分类号：

摘要：

Data mining based on association rules has achieved a good reputation in the performance optimization of the steam turbines in thermal power plant, but with the advent of the era of big data, traditional data mining methods cannot complete the data mining tasks with massive data due to its defects. To solve this problem, the paper introduced the attribute reduction in rough set theory, and then improved the classical Apriori association rules algorithm on the MapReduce framework of the Hadoop platform under a cloud computing environment. Using this framework, a new algorithm that can deal with the massive data mining tasks was produced. This paper examined use of the CC.Apriori algorithm in mining the historical data under a number of typical loads for a 1000MW ultra-supercritical unit. Then the relationship between operating parameters and performance indicators was produced and used to guide optimization operation of the stream turbines. Mining results show that the new algorithm can be effectively applied in the determination of the optimization targeted values to achieve the purpose of energy savings. The obtained optimization targeted values are from the actual operation of the unit and they are representative of optimum operation conditions. © 2016 Chin. Soc. for Elec. Eng.

引用

页码：459 / 467

页数：8

共 33 条

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