ANN-Based Modeling for Load and Main Steam Pressure Characteristics of a 600MW Supercritical Power Generating Unit

DOI

10.2991/iccsee.2013.731How to use a DOI?

Keywords

supercritical power unit, nonlinear mathematical model, artificial neural network, coordinated control system

Abstract

A supercritical coal-fired power generating unit is a typical multivariable system with large inertia and non-linear, slow time-variant, time-delay characteristics, which often makes the coordinated control quality deteriorate under wide-range load-changing conditions, and can't well satisfy the unit load and main steam pressure control requirements. Thus, it is of vital significance to study the supercritical boiler unit’s operation characteristic by means of modeling method, and to improve the control quality with model-based advanced control strategies. In this paper, artificial neural network (ANN) method was used to build a nonlinear mathematical model of the load and main steam pressure characteristics for a 600MW supercritical boiler unit. Operation data over wide-range load-changing conditions were used for model training. Simulation tests showed that the model can fit the complex non-linear, dynamic characteristics between the unit’s load, main steam pressure and fuel, feedwater flow and turbine governing valve opening with high precision and strong generalization ability. The model can be used as a prediction model to construct an intelligent controller for supercritical boiler unit coordinated control to meet the engineering application demand.

Copyright

© 2013, 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  - Liangyu Ma
AU  - Zhiyuan Gao
PY  - 2013/03
DA  - 2013/03
TI  - ANN-Based Modeling for Load and Main Steam Pressure Characteristics of a 600MW Supercritical Power Generating Unit
BT  - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)
PB  - Atlantis Press
SP  - 3179
EP  - 3183
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccsee.2013.731
DO  - 10.2991/iccsee.2013.731
ID  - Ma2013/03
ER  -