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.%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.
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