BP neural network based online prediction of steam turbine exhaust drynessBP neural network based online prediction of steam turbine exhaust dryness

XIE Danmei; CHEN Chang; XIONG Yangheng; GAO Shang; WANG Chun

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BP neural network based online prediction of steam turbine exhaust drynessBP neural network based online prediction of steam turbine exhaust dryness

热能科学研究 | 更新时间：2025-09-28

- BP neural network based online prediction of steam turbine exhaust drynessBP neural network based online prediction of steam turbine exhaust dryness
- BP neural network based online prediction of steam turbine exhaust drynessBP neural network based online prediction of steam turbine exhaust dryness
- 热力发电 2014年43卷第9期页码：43-47
- 作者机构：
  
  1. 1:School of Power and Mechanical Engineering
  2. 2:Wuhan University
- 作者简介：
- 基金信息：
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- 纸质出版日期：2014
- 稿件说明：
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XIE Danmei, CHEN Chang, XIONG Yangheng, 等. BP neural network based online prediction of steam turbine exhaust drynessBP neural network based online prediction of steam turbine exhaust dryness[J]. 热力发电, 2014,43(9):43-47.

BP neural network based online prediction of steam turbine exhaust drynessBP neural network based online prediction of steam turbine exhaust dryness[J]. Thermal power generation, 2014, 43(9): 43-47.
XIE Danmei, CHEN Chang, XIONG Yangheng, 等. BP neural network based online prediction of steam turbine exhaust drynessBP neural network based online prediction of steam turbine exhaust dryness[J]. 热力发电, 2014,43(9):43-47. DOI：

BP neural network based online prediction of steam turbine exhaust drynessBP neural network based online prediction of steam turbine exhaust dryness[J]. Thermal power generation, 2014, 43(9): 43-47. DOI：

摘要

In large scale condensing turbine unit

the exhaust status always lies in wet steam area.Due to the lack of effective measuring method

the exhaust dryness of the steam turbine is difficult to obtain directly

which has been the difficult problem in online economic analysis for thermal power units.By taking an N1000-25/600/600ultra-supercritical steam turbine as an example

the nonlinear mapping ability of BP neural network was used to establish a model which can reflect the relationship between exhaust dryness and unit load and exhaust pressure.After learning and training under some typical conditions

this model was used for exhaust dryness online calculation under full condition.The results show the final error of the training samples and verifying samples were controlled within-0.006 1and-0.001 0

which satisfies the accuracy requirement for engineering calculation

indicating the established BP neural network can be used in exhaust dryness prediction.

Abstract

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