|本期目录/Table of Contents|

[1]白建云a,孟新雨b,雷秀军a,等.基于BP神经网络的直接空冷凝汽器出口风温预测[J].工业仪表与自动化装置,2019,(02):63-67.[doi:1000-0682(2019)02-0000-00]
　BAI Jianyuna,MENG Xinyub,LEI Xiujuna,et al.Prediction of outlet air temperature of direct air condenser based on BP neural network[J].Industrial Instrumentation & Automation,2019,(02):63-67.[doi:1000-0682(2019)02-0000-00]
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基于BP神经网络的直接空冷凝汽器出口风温预测

《工业仪表与自动化装置》[ISSN:1000-0682/CN:61-1121/TH]

卷:
期数:
2019年02期
页码:
63-67
栏目:
出版日期:
2019-04-15

文章信息/Info

Title:
Prediction of outlet air temperature of direct air condenser based on BP neural network
作者:
白建云a; 孟新雨b; 雷秀军a; 苑文鑫b; 任 岐a
山西大学 a. 自动化系；b. 数学科学学院，太原 030013
Author(s):
BAI Jianyuna;  MENG Xinyub;  LEI Xiujuna;  YUAN Wenxinb;  REN Qia
a. Department of Automation;?
b. School of Mathematical Sciences, Shanxi University, Taiyuan 030013, China
关键词:
直接空冷凝汽器; BP神经网络; 出口风温; 预测模型
Keywords:
direct air-cooled condenser;  BP neural network;  outlet wind temperature;  prediction model
分类号:
TK39
DOI:
1000-0682(2019)02-0000-00
文献标志码:
A
摘要:
由于直接空冷系统的动态参数监测技术比较单一，为了便于对出口风温测点的研究，提出一种基于BP神经网络的空冷凝汽器出口风温预测模型。以某矸石电厂2×300 MW直接空冷凝汽器为对象进行研究，分别从锅炉侧与凝汽器侧分析影响空冷凝汽器出口风温的的因素，从现场DCS系统分别采集各因素历史数据，对数据进行预处理后，运用BP神经网络建立空冷凝汽器出口风温预测模型。用现场实际运行数据验证模型的有效性。该模型可以预测不同工况下的空冷凝汽器的出口风温，为下一步制定动态参数监测技术提供依据，合理安排空冷凝汽器温度测点的位置和数量，也可以为空冷凝汽器的清洗周期做一个参考。
Abstract:
The dynamic parameter monitoring technology for direct air cooling system is relatively simple.In order to facilitate the research of outlet wind temperature measurement point,this paper proposes a BP air network based air conditioning model for air condenser cooling.Taking a coal gangue power plant 2×300 MW direct air-cooled steam condenser as an object for research.The factors affecting the air temperature at the outlet of the air condenser are analyzed from the boiler side and the condenser side respectively.The historical data of each factor are collected from the on-site DCS system.After the data is preprocessed, the BP neural network is used to establish the air temperature prediction model of the air condenser.The actual operational data of the site is used to verify the validity of the model. The model can predict the outlet air temperature of the air condenser under different working conditions, provide a basis for the next step of developing dynamic parameter monitoring technology, and reasonably arrange the position and quantity of the air condenser temperature measuring point, or it can be an air condenser. The cleaning cycle is a reference.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期：2019-02-18
基金项目：国家自然科学基金项目（U1610116）；山西省科技重大专项基金资助项目（MD2016-02）
作者简介：白建云（1962），女，山西人，学士，教授，主要研究方向为大型发电机组智能优化控制。
更新日期/Last Update: 2019-04-15