供热机组多元工况下能效状态基准研究

Research on energy efficiency state benchmark of heating units under multiple operating conditions

为响应国家“双碳”战略,供热机组的优化运行对于节能减排有重要意义,如何确定真实可靠的能效状态基准值是运行优化的关键所在。基于数据挖掘算法,对机组历史数据进行分析:首先,以负荷和供热流量为特征量,采用区间估计的方法进行稳态筛选;其次,针对机组供热期和纯凝期,采用等间隔分类方法对其进行工况划分;然后,对每个稳态工况内的数据,采用高斯混合模型(Gaussian mixture model,GMM)算法模型,选择出热耗率最低时的样本作为基准样本,基于基准样本进一步估计其概率密度分布,从而获得各工况下能效状态的基准区间;采用BP算法建立能效状态基准回归模型,从而建立机组多元工况下的基准值工况库。最后,选取某320 MW供热机组历史数据进行方法验证和在线实时监测。分析结果表明,该方法可以优化机组运行,达到节能减排的目标。

In response to the national dual-carbon strategy,the optimized operation of heating units is of great significance to energy conservation and emission reduction.How to determine the true and reliable benchmark value of energy efficiency status is the key to operation optimization.Based on the data mining algorithm,the historical data of the unit is analyzed.Firstly,by taking load and heating flow as the characteristic quantities,the interval estimation method is applied for steady-state detection.Secondly,for the heating period and the pure condensation period of the unit,the equal interval classification method is used to divide the working conditions.Then,for the data in each steady-state operating condition,the Gaussian mixture model(GMM)algorithm model is used to select the sample with the lowest heat consumption as the reference sample.Based on the reference sample,the probability density distribution is further estimated to obtain the benchmark of the energy efficiency state under each operating condition interval.The BP algorithm is used to establish the energy efficiency state benchmark regression model,so as to establish the benchmark value working condition library under multiple working conditions of the unit.Finally,the historical data of a 320 MW heating unit is selected for method verification and online real-time monitoring.The analysis results show that,the method can optimize the operation of the unit,so as to achieve the goal of energy saving and emission reduction.