[1]田松峰,吴昭延,王子光,等. 基于神经网络的凝汽器污垢热阻预测模型[J].热力发电,2018,(预出版):1-5.[doi:10.19666/j.rlfd.201807145]
 TIAN Songfeng,WU Zhaoyan,WANG Ziguang,et al.Prediction model of condenser fouling thermal resistance based on neural network[J].Thermal Power Generation,2018,(预出版):1-5.[doi:10.19666/j.rlfd.201807145]
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 基于神经网络的凝汽器污垢热阻预测模型()
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《热力发电》[ISSN:1000-9035/CN:22-1262/O4]

卷:
期数:
2018年预出版
页码:
1-5
栏目:
出版日期:
2018-12-28

文章信息/Info

Title:
Prediction model of condenser fouling thermal resistance based on neural network
作者:
 田松峰吴昭延王子光王傲男魏 言
 华北电力大学电站设备状态监测与控制教育部重点实验室,河北 保定 071003
Author(s):
 TIAN Songfeng WU Zhaoyan WANG Ziguang WANG Aonan WEI Yan
 MOE’s Key Lab of Condition Monitoring and Control for Power Plant Equipment, North China Electric Power University, Baoding 071003, China
关键词:
 Elman神经网络粒子群算法凝汽器污垢热阻预测模型
分类号:
TK264.1
DOI:
10.19666/j.rlfd.201807145
文献标志码:
A
摘要:
 针对凝汽器污垢热阻难以预测的问题,采用改进粒子群算法优化的Elman神经网络建立凝汽器污垢热阻预测模型。根据粒子个体与全局的认知能力动态调整惯性权重,改进粒子群算法,提高算法收敛精度和速率,利用改进的粒子群算法优化神经网络的权值和阀值,提升模型的预测能力。以某电厂300 MW机组凝汽器清洗后的运行状况搭建模型,将预测值与实际值进行对比,验证其准确性。结果表明,改进后的预测模型具有更好的精度和适应能力,为凝汽器污垢热阻预测和清洗时间间隔提供了理论依据。

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备注/Memo

备注/Memo:
 田松峰(1966—),男,教授,博士,主要研究方向为电站设备节能与监测。
更新日期/Last Update: 2018-09-17