A practical NMPC with robustness of stability applied to distributed solar power plants

Abstract This paper presents the application of a Nonlinear Model Predictive Controller (NMPC) to a distributed solar collector field. The control technique is basically similar to Dynamic Matrix Control (DMC) but in the proposed approach a nonlinear model of the process is directly used without linearization of the process model involved in the control strategy. Moreover, a modified Practical Nonlinear Model Predictive Controller (PNMPC) algorithm adapted to solar plant is developed in this work. To include robustness of stability against uncertainties in the NMPC algorithm, a candidate Lyapunov function is included in the cost function. The main purpose of the controller is to manipulate the oil flow rate to maintain the field outlet temperature in the desired reference value and attenuate the disturbances effects. The simulated process used is a distributed parameter model, while for the prediction a lumped parameter model with time delay was considered.

[1]  J. Henriques,et al.  Adaptive neural model-based predictive control of a solar power plant , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[2]  Tamara G. Kolda,et al.  Optimization by Direct Search: New Perspectives on Some Classical and Modern Methods , 2003, SIAM Rev..

[3]  Ilya V. Kolmanovsky,et al.  Predictive energy management of a power-split hybrid electric vehicle , 2009, 2009 American Control Conference.

[4]  João M. Lemos,et al.  OBSERVER BASED NONUNIFORM SAMPLING PREDICTIVE CONTROLLER FOR A SOLAR PLANT , 2002 .

[5]  Edoardo Mosca,et al.  Integrating Predictive and Switching Control: Basic Concepts and an Experimental Case Study , 2000 .

[6]  Manuel Berenguel,et al.  A survey on control schemes for distributed solar collector fields. Part II: Advanced control approaches , 2007 .

[7]  M. Berenguel,et al.  Application of generalized predictive control to a solar power plant , 1994, 1994 Proceedings of IEEE International Conference on Control and Applications.

[8]  Julio E. Normey-Rico,et al.  A PRACTICAL APPROACH TO PREDICTIVE CONTROL FOR NONLINEAR PROCESSES , 2007 .

[9]  Manuel Berenguel,et al.  Advanced control of solar plants , 1997 .

[10]  Soteris A. Kalogirou,et al.  Applications of artificial neural-networks for energy systems , 2000 .

[11]  Manuel Berenguel,et al.  Feedback linearization control for a distributed solar collector field , 2005 .

[12]  D. Kearney,et al.  Survey of Thermal Energy Storage for Parabolic Trough Power Plants , 2002 .

[13]  R. Pickhardt Application of adaptive controllers to a solar power plant using a multi-model description , 1998, Proceedings of the 1998 American Control Conference. ACC (IEEE Cat. No.98CH36207).

[14]  Eduardo F. Camacho,et al.  Estimation of effective solar irradiation using an unscented Kalman filter in a parabolic-trough field , 2012 .

[15]  Robain De Keyser,et al.  Nonlinear predictive control with dead-time compensator: Application to a solar power plant , 2009 .

[16]  David Q. Mayne,et al.  Model predictive control: Recent developments and future promise , 2014, Autom..

[17]  Manuel Berenguel,et al.  Control of Solar Energy Systems , 2012 .

[18]  Manuel Berenguel,et al.  A survey on control schemes for distributed solar collector fields. Part I: Modeling and basic control approaches , 2007 .