Neural network approach for predicting drum pressure and level in coal-fired subcritical power plant

There is increasing need for tighter controls of coal-fired plants due to more stringent regulations and addition of more renewable sources in the electricity grid. Achieving this will require better process knowledge which can be facilitated through the use of plant models. Drum-boilers, a key component of coal-fired subcritical power plants, have complicated characteristics and require highly complex routines for the dynamic characteristics to be accurately modelled. Development of such routines is laborious and due to computational requirements they are often unfit for control purposes. On the other hand, simpler lumped and semi empirical models may not represent the process well. As a result, data-driven approach based on neural networks is chosen in this study. Models derived with this approach incorporate all the complex underlying physics and performs very well so long as it is used within the range of conditions on which it was developed. The model can be used for studying plant dynamics and design of controllers. Dynamic model of the drum-boiler was developed in this study using NARX neural networks. The model predictions showed good agreement with actual outputs of the drum-boiler (drum pressure and water level).

[1]  Mark O'Malley,et al.  A drum boiler model for long term power system dynamic simulation , 1999 .

[2]  Wojciech Stanek,et al.  Hybrid model of steam boiler , 2010 .

[3]  Xiangjie Liu,et al.  The dynamic neural network model of a ultra super-critical steam boiler unit , 2011, Proceedings of the 2011 American Control Conference.

[4]  Yucai Zhu,et al.  Multivariable System Identification For Process Control , 2001 .

[5]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[6]  Wojciech Stanek,et al.  Neural modelling of steam boilers , 2007 .

[7]  B. W. Hogg,et al.  Identification of boiler models , 1989 .

[8]  Julian F Dunne,et al.  NARX Neural Network Modelling of Hydraulic Suspension Dampers for Steady-state and Variable Temperature Operation , 2003 .

[9]  George W. Irwin,et al.  Neural network modelling of a 200 MW boiler system , 1995 .

[10]  Mohsen Assadi,et al.  Development of an artificial neural network model for the steam process of a coal biomass cofired combined heat and power (CHP) plant in Sweden , 2007 .

[11]  Luis M. Romeo,et al.  Neural network for evaluating boiler behaviour , 2006 .

[12]  Mohsen Assadi,et al.  Development of artificial neural network model for a coal-fired boiler using real plant data , 2009 .

[13]  Michele Pinelli,et al.  Modeling and Simulation of the Start-Up Operation of a Heavy-Duty Gas Turbine by Using NARX Models , 2014 .

[14]  F. P. de Mello,et al.  Boiler models for system dynamic performance studies , 1991 .

[15]  Guolian Hou,et al.  Modeling of a 1000MW power plant ultra super-critical boiler system using fuzzy-neural network methods , 2013 .

[16]  B. W. Hogg,et al.  Dynamic nonlinear modelling of power plant by physical principles and neural networks , 2000 .

[17]  Laura Giarré,et al.  NARX models of an industrial power plant gas turbine , 2005, IEEE Transactions on Control Systems Technology.

[18]  Dragan Antić,et al.  Simulation Model of Magnetic Levitation Based on NARX Neural Networks , 2013 .

[19]  Meihong Wang,et al.  Dynamic modelling, validation and analysis of coal-fired subcritical power plant , 2014 .

[20]  Les E. Atlas,et al.  Recurrent neural networks and robust time series prediction , 1994, IEEE Trans. Neural Networks.

[21]  Eric Garnier,et al.  NARX modelling of unsteady separation control , 2013 .

[22]  Inmaculada Arauzo,et al.  Monitoring and prediction of fouling in coal-fired utility boilers using neural networks , 2005 .

[23]  Yanjun Fang,et al.  T-S Neural Network Model Identification of Ultra-Supercritical Units for Superheater Based on Improved FCM , 2012 .

[24]  Karl Johan Åström,et al.  Drum-boiler dynamics , 2000, Autom..