热力发电2017，Vol.46Issue(6)：28-33,6.DOI:10.3969/j.issn.1002-3364.2017.06.028
基于改进k-means算法的电站最优外部运行工况划分
Application of improved k-means algorithm in optimal operation of power plant
摘要
Abstract
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.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Key words
power station/historical operating data/optimal external operating condition/data mining/k-means algorithm/equal width method/condition division分类
信息技术与安全科学引用本文复制引用
秦绪华,王秋平,陈志强..基于改进k-means算法的电站最优外部运行工况划分[J].热力发电,2017,46(6):28-33,6.基金项目
国家自然科学青年基金项目(61503072) （61503072）
吉林省科技厅自然基金项目(20150101048JC) National Natural Science Foundation of China (61503072) （20150101048JC）
Natural Science Foundation of Jilin Province Science and Technology Department (20150101048JC) （20150101048JC）
