基于IGWO-SVM的汽轮机低负荷下主蒸汽压力优化研究  

Optimization on Main Steam Pressure of a Steam Turbine Under Low Loads Based on IGWO-SVM

作　　者：吴瑞康 刘迪 郑建平 童家麟 叶学民[2] WU Ruikang;LIU Di;ZHENG Jianping;TONG Jialin;YE Xuemin(Hangzhou E-Energy Technology Co.,Ltd.,Hangzhou,310014,China;North China Electric Power University,Baoding 071003,Hebei Province,China)

机构地区：[1]杭州意能电力技术有限公司,杭州310014 [2]华北电力大学动力工程系,河北保定071003

出　　处：《动力工程学报》2024年第7期1042-1050,共9页Journal of Chinese Society of Power Engineering

基　　金：杭州意能电力技术有限公司科技资助项目(EERD2022-12)。

摘　　要：为提高汽轮机低负荷下的运行效率,需要对主蒸汽压力进行优化。根据机组实际运行数据,采用支持向量机(SVM)算法建立了热耗率预测模型,并利用改进的灰狼优化(IGWO)算法优化SVM模型超参数;在此基础上,利用IGWO算法在低负荷下的可行压力区间进行寻优,得到了优化后的汽轮机滑压曲线,并且进行了实例验证。结果表明:利用IGWO算法优化的热耗率预测模型能够对低负荷下的热耗率进行准确预测;优化后机组在低负荷下的热耗率均有所下降,在负荷为223.83 MW时,热耗率降低了505.96 kJ/(kW·h),降低幅度最大。研究结果表明所提的优化方案可以有效提高汽轮机低负荷下的热经济性。To improve the operating efficiency of steam turbine under low loads,it is necessary to optimize the main steam pressure.A heat rate prediction model was established by support vector machine(SVM)algorithm based on actual operating data of a unit.The improved grey wolf optimization(IGWO)algorithm was used to optimize the hyperparameters of the SVM model.The IGWO algorithm was used to optimize the feasible pressure range under low loads,and the optimized steam turbine sliding pressure curve was obtained and verified by a practical example.Results show that,the heat rate prediction model optimized using the IGWO algorithm can accurately predict the heat rate under low loads.After optimization,the heat rate of the unit is decreased under low loads,especially when the load is 223.83 MW,the heat rate is decreased by 505.96 kJ/(kW·h),presenting the largest reduction.The optimization scheme proposed can effectively improve the thermal economy of steam turbine under low loads.

关 键 词：汽轮机 低负荷 主蒸汽压力 灰狼优化算法 支持向量机 

分 类 号：TM621[电气工程—电力系统及自动化]