摘要
轴流转桨式水轮机组效率与水轮机的导叶-桨叶协联关系有着密切的关系。目前水轮机的协联关系是通过线性插值法取得的,其值与水轮机最优运行工况的协联关系有较大差异。针对插值法结果不能正确体现最优协联关系的缺点,利用粒子群神经网络(PSO-BP)对导叶-桨叶关系进行训练测试,获得导叶、桨叶之间的协联关系。通过水轮机组试验,PSO-BP算法的桨叶开度值比线性插值法的桨叶开度值平均减少3.61%,效率提高了3.09%。实验结果表明PSO-BP优化后导叶-桨叶协联关系可使水轮机的过水流量减少,节约了水力资源,提高了水轮机的运行效率。
The efficiency of Kaplan turbine units is closely related to the guide vane-paddle coupling relationship of the turbine.At present,the coupling relationship of hydraulic turbine is obtained by linear interpolation method,and its value is quite different from that of hydraulic turbine in optimal operating condition.In view of the shortcoming that the interpolation method cannot correctly reflect the optimal co-coupling relationship,this paper makes use of particle swarm neural network(PSO-BP)to train and test original relationship between guide vane and paddle,and obtains better coupling relationship.Through the hydraulic turbine group test,the paddle opening value of PSO-BP algorithm is reduced by 3.61%on average than that of linear interpolation method,and the efficiency is improved by 3.09%.The experimental results show that the PSO-BP optimized guide vane-paddle coupling relationship can reduce overwater flow,save hydraulic resource and improve operating efficiency of the hydraulic turbine.
作者
孙江
韩泓
SUN Jiang;HAN Hong(Shanxi Institute of Energy,Department of Electrical and Control Engineering,Jinzhong 030600,China)
出处
《电工技术》
2024年第6期23-25,35,共4页
Electric Engineering
基金
山西省水利科学技术研究与推广项目(编号2023GW45)。
