基于MFOA-GRNN的汽轮机热耗率预测模型  

Prediction Model of Steam Turbine Heat Rate based on MFOA-GRNN

作　　者：钟震 童小忠 司风琪[1] 任少君[1] ZHONG Zhen;TONG Xiao-zhong;SI Feng-qi;REN Shao-jun(Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education,Southeast University,Nanjing,Jiangsu,China,210096;Zhejiang Energy Group Research Institute,Hangzhou,Zhejiang,China,310012)

机构地区：[1]东南大学能源转换及其过程测控教育部重点实验室,江苏南京210096 [2]浙江浙能技术研究院有限公司,浙江杭州311121

出　　处：《热能动力工程》2020年第2期63-69,86,共8页Journal of Engineering for Thermal Energy and Power

摘　　要：为提高机组热耗率在线计算的精度与鲁棒性,提出多种群果蝇优化算法(Multi-population fruit fly optimization algorithm,MFOA)和广义回归神经网络(Generalized regression neural network,GRNN)相结合的汽轮机热耗率预测模型。以影响机组热耗率的主要运行参数为输入参数,建立基于GRNN的机组热耗率计算模型,并进一步采用改进的多种群果蝇优化算法优化GRNN模型中的光滑因子。将所建MFOA-GRNN热耗率预测模型应用到某1000 MW机组中,结果表明该模型具有很好的计算精度,在测量数据发生方差增大、定值偏移等异常情况时该模型也能给出可靠的计算结果,具有较强的泛化能力和鲁棒性,满足实际工程需要。In order to improve the accuracy and robustness of online calculation of unit heat rate,a prediction model of steam turbine heat rate based on multi-population fruit fly optimization algorithm(MFOA)and generalized regression neural network(GRNN)is proposed.Taking the main operating parameters affecting the unit heat rate as input parameters,the calculation model of unit heat rate based on GRNN is established,and the smoothing factor in GRNN model is further optimized by the improved multi-population fruit fly optimization algorithm.The built MFOA-GRNN heat rate prediction model is applied to a 1000 MW unit,and the results show that the model has good calculation accuracy.The model can also generate reliable calculation results when the variance of measured data increases or the fixed value migration occurs.It shows that the proposed model has strong generalization ability and robustness,which can meet the actual engineering needs.

关 键 词：热耗率 果蝇优化算法 广义回归神经网络 泛化能力 光滑因子 预测模型 

分 类 号：TK261[动力工程及工程热物理—动力机械及工程]