ANN-Based Modeling for Load and Main Steam Pressure Characteristics of a 600MW Supercritical Power Generating Unit
Liangyu Ma, Zhiyuan Gao
Available Online March 2013.
- https://doi.org/10.2991/iccsee.2013.731How to use a DOI?
- supercritical power unit, nonlinear mathematical model, artificial neural network, coordinated control system
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 PB - Atlantis Press SP - 3179 EP - 3183 SN - 1951-6851 UR - https://doi.org/10.2991/iccsee.2013.731 DO - https://doi.org/10.2991/iccsee.2013.731 ID - Ma2013/03 ER -