应用 PSO 算法改进 Elman 神经网络的双压凝汽器真空预测

摘要

为实现对凝汽器真空的优化控制，引入一种采用粒子群优化（PSO）算法改进的 Elman 神经网络，建立双压凝汽器真空预测模型，提出对双压凝汽器高、低压侧真空分别进行预测计算，将该模型应用于某600 MW 机组的双压凝汽器真空预测，并与普通算法改进的 Elman 神经网络的预测结果进行比较。结果表明：采用 PSO 算法改进的 Elman 神经网络对双压凝汽器高、低压侧真空预测的收敛速度更快、精确度更高，是一种行之有效的双压凝汽器真空预测模型。%An Elman neural network optimized by particle swarm optimization (PSO)algorithm was introduced to establish the vacuum prediction model for dual-pressure condensers.The calculation model which can forecast both the high pressure and low pressure side of the dual-pressure condensers was proposed.Moreover,the above model was applied in dual-pressure condenser in a 600 MW unit and the results were compared with that predicted by the common algorithm-modified Elman neural network.The results show that,this Elman neural network optimized by PSO algorithm has faster convergence speed and higher accuracy,which is a feasible model for vaccum prediction in dual-pressure condensers.