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Benchmark value determination of energy efficiency indexes for coal-fired power units based on data mining methods

机译:基于数据挖掘方法的燃煤电厂能效指标基准值确定

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摘要

The operational optimisation of coal-fired power units is important for saving energy and reducing losses in the electric power industry. One of the key issues is how to determine the benchmark values of the energy efficiency indexes of the units. Therefore, a new framework for determining these benchmark values is proposed, based on data mining methods. First, the energy efficiency key performance indicators (KPIs) associated with the net coal consumption rate (NCCR) were selected based on the domain knowledge. Second, the decision-making samples with minimal NCCR were acquired with the fuzzy C-means (FCM) clustering algorithm, and the corresponding clustering centres were employed as the benchmark values. Finally, based on the support vector regression (SVR) algorithm, the target values of the NCCR were obtained with the KPIs as input, and the energy saving potential was evaluated by comparing the target values with the historical values of the NCCR. An actual on-duty 1000 MW unit was taken as study unit, and the results show that the energy saving potential is remarkable when the operators adjust the KPIs based on the calculated benchmark values.
机译:燃煤发电机组的运行优化对于节省能源和减少电力行业的损失至关重要。关键问题之一是如何确定设备的能效指标的基准值。因此,提出了一种基于数据挖掘方法确定这些基准值的新框架。首先,根据领域知识选择与煤炭净消耗率(NCCR)相关的能效关键绩效指标(KPI)。其次,采用模糊C均值(FCM)聚类算法获取NCCR最小的决策样本,并以相应的聚类中心为基准值。最后,基于支持向量回归(SVR)算法,以KPI为输入获得NCCR的目标值,并通过将目标值与NCCR的历史值进行比较来评估节能潜力。以实际值班的1000 MW机组为研究单元,结果表明,当操作员根据计算出的基准值调整KPI时,节能潜力显着。

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