Particle Swarm Optimization (PSO) Based Turbine Control

Ali Tarique, Hossam A. Gabbar

Faculty of Energy Systems and Nuclear Science, University of Ontario Institute of Technology, Oshawa, Canada.
Faculty of Engineering and Applied Science, University of Ontario Institute of Technology, Oshawa, Canada.

DOI: 10.4236/ica.2013.42018                

Abstract

The steam turbine control system is strongly non-linear in all operating conditions. Proportional-Integral-Derivative (PID) controller that is currently used in control systems of many types of equipment is not considered highly precision for turbine speed control system. A fine tuning of the PID controller by some optimization technique is a desired objective to maintain the precise speed of the turbine in a wide range of operating conditions. This Paper evaluates the feasibility of the use of Particle Swarm Optimization (PSO) method for determining the optimal Proportional-Integral-Derivative (PID) controller parameters for steam turbine control. The turbine speed control is modelled in Simulink^TM with PID controller and the PSO algorithm is implemented in MATLAB to optimize the PID function. The PSO optimization technique is also compared with Genetic Algorithm (GA) and it is validated that PSO based controller is more efficient in reducing the steady-states error; settling time, rise time, and overshoot limit in speed control of the steam turbine control.

Keywords

PSO; PID Controller; GA; Optimization

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	K. J. Astrom and T. Hagglund, “PID Controllers, Theory, Design and Tuning,” 2nd Edition, Instrument Society of America, 1995.
[2]	O. Aydogdu and M. Korkmaz, “A Simple Approach to Design of Variable Parameter Nonlinear PID Controller,” International Conference on Advancement in Information Technology, Singapore, 2011.
[3]	H. B. Liu, “Intelligent Coordinated Control of PowerPlant Main Steam Pressure and Power Output,” Journal of Systems Engineering and Electronics, Vol. 15, No. 3, 2004, pp. 350-358.
[4]	J. A. Nelder and R. A. Mead, “A Simplex Method for Function Minimization,” Computer Journal, Vol. 7, No. 4, 1964, pp. 308-313.
[5]	V. R. Sabanin, N. I. Smirnov and A. I. Repin, “Optimization of Settings of Regulative Devices in ASR,” Collection of Labours of Conference of Control, MEI, 2003, pp. 144-148.
[6]	D. E. Goldberg, “Genetic Algorithms in Search Optimizations and Machine Learning,” Wesly, Addison, 1989.
[7]	G. K. Voronovskiy, K. V. Makhotilo, S. N. Petrashev and S. A. Sergeev, “Genetic Algorithms, Artificial Neuron Networks and Problems of Virtual Reality,” Basis, Kharkov, 1997.
[8]	C. L. Lin, H. Y. Jan and N. C. Shieh, “GA-Based Multi Objective PID Control for a Linear Brushless DC Motor,” IEEE/ASME Transactions on Mechatronics, Vol. 8, No. 1, 2003, pp. 56-65.
[9]	M. Nasri, H. Nezamabadi-Pour and M. Maghfoori, “A PSO-Based Optimum Design of PID Controller for a Linear Brushless DC Motor,” World Academy of Science, Engineering and Technology, Vol. 20, 2007, pp. 211215.
[10]	D. B. Fogel, “Evolutionary Computation toward a New Philosophy of Machine Intelligence,” IEEE, New York, 1995.
[11]	J. Kennedy and R. Eberhart, “Particle Swarm Optimization,” Proceedings of IEEE of International Conference on Neural Networks, Vol. 4, Perth, 1995, pp. 1942-1948. doi:10.1109/ICNN.1995.488968
[12]	A. Banks, J. Vincent and Ch. Anyakoha, (2007) “A Review of Particle Swarm Optimization. Part I: Background and Development,” Natural Computing, Vol. 6, No. 4, pp. 467-484. doi:10.1007/s11047-007-9049-5
[13]	M. A. Abido, “Optimal Design of Power-System Stabilizers Using Particle Swarm Optimization,” IEEE Transactions on Energy Conversion, Vol. 17, No. 3, 2002, pp. 406-413. doi:10.1109/TEC.2002.801992
[14]	J. Yang and A. Bouzerdoum, “A Particle Swarm Optimization Algorithm Based on Orthogonal Design,” IEEE Congress on Evolutionary Computation, 18-23 July 2010.
[15]	PSO Tutorial, 2012. http://www.swarmintelligence.org/tutorials.php