Distributed Model Predictive Control of Steam/Water Loop in Large Scale Ships

In modern steam power plants, the ever-increasing complexity requires great reliability and flexibility of the control system. Hence, in this paper, the feasibility of a distributed model predictive control (DiMPC) strategy with an extended prediction self-adaptive control (EPSAC) framework is studied, in which the multiple controllers allow each sub-loop to have its own requirement flexibility. Meanwhile, the model predictive control can guarantee a good performance for the system with constraints. The performance is compared against a decentralized model predictive control (DeMPC) and a centralized model predictive control (CMPC). In order to improve the computing speed, a multiple objective model predictive control (MOMPC) is proposed. For the stability of the control system, the convergence of the DiMPC is discussed. Simulation tests are performed on the five different sub-loops of steam/water loop. The results indicate that the DiMPC may achieve similar performance as CMPC while outperforming the DeMPC method.

[1]  Li Sun,et al.  Multi-objective optimization for advanced superheater steam temperature control in a 300 MW power plant , 2017 .

[2]  Jiong Shen,et al.  Extended state observer based fuzzy model predictive control for ultra-supercritical boiler-turbine unit , 2017 .

[3]  Godpromesse Kenné,et al.  Coordinated excitation and steam valve control for multimachine power system using high order sliding mode technique , 2016 .

[4]  Sheng Liu,et al.  Effect of Control Horizon in Model Predictive Control for Steam/Water Loop in Large-Scale Ships , 2018, Processes.

[5]  Kazimierz Duzinkiewicz,et al.  Multi-region fuzzy logic controller with local PID controllers for U-tube steam generator in nuclear power plant , 2015 .

[6]  G. R. Ansarifar,et al.  Sliding mode control design for a PWR nuclear reactor using sliding mode observer during load following operation , 2015 .

[7]  Eduardo Camponogara,et al.  Controlling Networks with Collaborative Nets , 2000 .

[8]  Concepción A. Monje,et al.  Fractional order fuzzy-PID control of a combined cycle power plant using Particle Swarm Optimization algorithm with an improved dynamic parameters selection , 2017, Appl. Soft Comput..

[9]  Mohammad Hassan Asemani,et al.  Robust Tracking Control of Boiler-Turbine Systems , 2019, 2019 6th International Conference on Control, Instrumentation and Automation (ICCIA).

[10]  Amanullah M. T. Oo,et al.  Non‐linear adaptive coordinated controller design for multimachine power systems to improve transient stability , 2016 .

[11]  Lieve Helsen,et al.  Controlling district heating and cooling networks to unlock flexibility: A review , 2018 .

[12]  Li Sun,et al.  Direct energy balance based active disturbance rejection control for coal-fired power plant. , 2017, ISA transactions.

[13]  G. R. Ansarifar,et al.  Control of the nuclear steam generators using adaptive dynamic sliding mode method based on the nonlinear model , 2016 .

[14]  Dana Copot,et al.  Simple Alternatives to PID-Type Control for Processes with Variable Time-Delay , 2019, Processes.

[15]  Hamed Moradi,et al.  Sliding mode control of drum water level in an industrial boiler unit with time varying parameters: A comparison with H∞-robust control approach , 2012 .

[16]  Xiangjie Liu,et al.  Economic model predictive control of boiler-turbine system , 2018, Journal of Process Control.

[17]  R. Smierzchalski.  Simulation system for marine engine control room , 2008, 2008 11th International Biennial Baltic Electronics Conference.

[18]  Bruce H. Krogh,et al.  Distributed model predictive control , 2001, Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148).

[19]  Robin De Keyser,et al.  Nonlinear Predictive Control Applied to Steam/Water Loop in Large Scale Ships , 2019 .

[20]  Xiaobing Kong,et al.  Nonlinear multivariable hierarchical model predictive control for boiler-turbine system , 2015 .

[21]  Mohammad Hossein Kazemi,et al.  Fractional order PID control of steam generator water level for nuclear steam supply systems , 2019 .

[22]  Sun Liying,et al.  Direct Fuzzy Backstepping Control for Turbine Main Steam Valve of Multi-machine Power System , 2015, 2015 2nd International Conference on Information Science and Control Engineering.

[23]  Li Sun,et al.  On Tuning and Practical Implementation of Active Disturbance Rejection Controller: A Case Study from a Regenerative Heater in a 1000 MW Power Plant , 2016 .

[24]  Clara M. Ionescu,et al.  The 5W’s for Control as Part of Industry 4.0: Why, What, Where, Who, and When—A PID and MPC Control Perspective , 2019, Inventions.

[25]  Katarzyna Mazur,et al.  Secure and Time-Aware Communication of Wireless Sensors Monitoring Overhead Transmission Lines , 2017, Sensors.

[26]  Chuanhou Gao,et al.  Guest Editorial: Special section on data-driven approaches for complex industrial systems , 2013, IEEE Trans. Ind. Informatics.

[27]  Eduardo Camponogara,et al.  Collaborative nets , 2000, Proceedings of the 33rd Annual Hawaii International Conference on System Sciences.

[28]  Antonella Ferrara,et al.  Sliding mode observers for a network of thermal and hydroelectric power plants , 2018, Autom..

[29]  Xiao Wu,et al.  Fuzzy modeling and predictive control of superheater steam temperature for power plant. , 2015, ISA transactions.

[30]  Vassilios G. Agelidis,et al.  Unified Distributed Control for DC Microgrid Operating Modes , 2016, IEEE Transactions on Power Systems.

[31]  Yasunori Mitani,et al.  SMES based a new PID controller for frequency stability of a real hybrid power system considering high wind power penetration , 2018, IET Renewable Power Generation.

[32]  Stephen J. Wright,et al.  Stability and optimality of distributed model predictive control , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[33]  Xinghuo Yu,et al.  Robust Nonlinear Adaptive Backstepping Coordinated Control for Boiler-Turbine Units , 2018, 2018 IEEE 27th International Symposium on Industrial Electronics (ISIE).

[34]  Hamed Moradi,et al.  Design & application of adaptive variable structure & H∞ robust optimal schemes in nonlinear control of boiler-turbine unit in the presence of various uncertainties , 2018 .

[35]  Zhiwei Gao,et al.  Advances in Modelling, Monitoring, and Control for Complex Industrial Systems , 2019, Complex..

[36]  Dana Copot,et al.  An industrially relevant formulation of a distributed model predictive control algorithm based on minimal process information , 2018, Journal of Process Control.