Application of PSO algorithm-modified Elman neural network in vacuum prediction for dual-pressure condensers

ZHANG Liping; CHEN Haotian; WANG Weifeng; LI Kaituo

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Application of PSO algorithm-modified Elman neural network in vacuum prediction for dual-pressure condensers

更新时间：2025-09-28

- Application of PSO algorithm-modified Elman neural network in vacuum prediction for dual-pressure condensers
- Thermal Power Generation Vol. 44, Issue 3, Pages: 53-57(2015)
- 作者机构：
  
  1. 华北水利水电大学电力学院
  2. 西安热工研究院有限公司
- 作者简介：
- 基金信息：
- DOI：
  CLC： TM621
- Published：2015
- 稿件说明：
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ZHANG Liping, CHEN Haotian, WANG Weifeng, et al. Application of PSO algorithm-modified Elman neural network in vacuum prediction for dual-pressure condensers[J]. Thermal power generation, 2015, 44(3): 53-57.
DOI：

ZHANG Liping, CHEN Haotian, WANG Weifeng, et al. Application of PSO algorithm-modified Elman neural network in vacuum prediction for dual-pressure condensers[J]. Thermal power generation, 2015, 44(3): 53-57. DOI：

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。

Abstract

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.

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