基于数据挖掘的燃煤机组能耗敏感性分析

Energy consumption sensitivity analysis of coal-fired power units based on big data mining

燃煤机组的能耗特性是机组节能降耗的主要指标。本文采用数据挖掘技术对机组历史运行数据进行分析,利用模糊粗糙集属性约简方法对影响机组能耗特性的运行特征参数进行约简,得到最简属性集。然后采用支持向量机技术建立机组能耗敏感性分析模型,分析不同负荷工况下各运行特征参数对供电煤耗的敏感性系数,为机组实际运行优化及节能降耗提供指导。以某600 MW燃煤机组为例进行能耗敏感性分析,结果表明:基于数据挖掘的机组能耗敏感性分析模型训练样本和测试样本的精度基本在±0.04%之间,精度均小于0.50%,该模型可行有效;在不同负荷工况下,各运行特征参数对供电煤耗的敏感性系数发生变化,且无明显变化规律,在机组实际运行优化中应根据敏感性系数的变化采取相应的调控措施。

The energy consumption characteristics of coal-fired power units are main indicators of energy saving and consumption reduction of power generation units.The big data mining technology was applied to analyze the historical operating data of the units.Based on the attribute reduction method of the fuzzy rough set,the operating characteristic parameters which affect the energy consumption characteristics of the units were reduced,and the simplest attribute set was obtained.Then,the sensitivity analysis model of the unit energy consumption was established by using the support vector machine technology.The sensitivity coefficient of the operating parameters to the coal consumption of power supply under different load conditions was analyzed,to provide guidance for the actual operation optimization and energy saving of the unit.Taking a 600 MW unit coal-fired unit as an example,the energy consumption sensitivity was analyzed.It is found that the precision of the test sample and the training sample of the unit energy consumption sensitivity analysis model based on data mining is basically within±0.04%,and the accuracy is less than 0.5%.The model is feasible and effective.In addition,the sensitivity coefficient of operating characteristic parameters to the coal consumption of power supply change greatly but irregularly under different load conditions.The corresponding adjustment measures should be taken according to the change of the sensitivity coefficient in the actual optimization operation of the unit.