On Neural Network Modeling of Main Steam Temperature for Ultra - supercritical Power Unit with Load Varying

DOI

10.2991/icsma-16.2016.77How to use a DOI?

Keywords

Ultra-supercritical unit; Main steam temperature; Operation data; Neural network; Modeling

Abstract

In order to solve the problem of accurate modeling of main steam temperature in ultra-supercritical (USC) unit under variable load conditions, the BP neural network model was established based on the field operation data. Considering the operation mechanism of the process, combing the relevant analysis of the sampled parameters, the feature parameters were chosen as the model inputs, the selected data was scaled to avoid the influence of different parameters range, then a 3 layer BP neural network model was established. By changing the number of hidden layer nodes in the neural network and the type of the hidden and the output layer activation function, the influence on the gained model performances under the variable load conditions was investigated. The simulation results on a 1000MW unit show that the model can reveal the main steam temperature changes in the actual production process.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xifeng Guo
AU  - Jingtao Huang
AU  - Lijie Wang
AU  - Jia Zhang
PY  - 2016/12
DA  - 2016/12
TI  - On Neural Network Modeling of Main Steam Temperature for Ultra - supercritical Power Unit with Load Varying
BT  - Proceedings of the 2016 4th International Conference on Sensors, Mechatronics and Automation (ICSMA 2016)
PB  - Atlantis Press
SP  - 431
EP  - 437
SN  - 1951-6851
UR  - https://doi.org/10.2991/icsma-16.2016.77
DO  - 10.2991/icsma-16.2016.77
ID  - Guo2016/12
ER  -