摘要
采用电站历史运行数据对外部运行工况进行划分的结果取决于挖掘算法对数据的适应性。本文提出了适用于电站历史运行数据外部工况划分的k-means算法,并对该算法的初始聚类数与聚类中心的计算方法进行分析改进,将其应用于某电站历史运行数据的机组负荷、煤质特性的外部工况的数据挖掘中,并采用等宽度法对电站外界环境温度历史运行数据进行聚类分析。挖掘结果表明,本文提出的改进k-means算法和等宽度法的工况划分结果更合理,且可得到描述机组运行的最优外部运行工况组合,能为现场运行人员提供更合理的数据参考依据。
The result of dividing external operating conditions by using the historical operating data of power plants depends on the adaptability of the mining algorithm to the data. In this paper, the k-means algorithm for the external operation of the historical operation data of the power stations was proposed, and the initial clustering numbers of the k-means algorithm and the calculation method of the clustering center were analyzed and improved. Moreover, this algorithm was applied in data mining of the external conditions of the unit running data for the power station, and the historical data of the external temperature of the power station was clustering analyzed by the equal width method. The mining results show that the improved k-means algorithm and the equal width method are more reasonable, and the optimal combination of external operating conditions can be obtained to describe the unit operation, which can provide more reasonable data references for the field operation personnel.
作者
秦绪华
王秋平
陈志强
QIN Xuhua WANG Qiuping CHEN Zhiqiang(Electric Power Research Institute of State Grid Jilin Electric Power Supply Company, Changchun 130000, China School of Automation Engineering, Northeast Electric Power University, Jilin 132012, China Electric Power Research Institute of State Grid Xinjiang Electric Power Supply Company, Urumqi 830001, China)
出处
《热力发电》
CAS
北大核心
2017年第6期28-33,共6页
Thermal Power Generation
基金
国家自然科学青年基金项目(61503072)
吉林省科技厅自然基金项目(20150101048JC)~~
关键词
电站
历史运行数据
最优外部运行工况
数据挖掘
K-MEANS算法
等宽度法
工况划分
power station, historical operating data, optimal external operating condition, data mining, k-means algorithm, equal width method, condition division
