Abstract

This study focuses on multi-objective optimization of the PID controllers for optimal speed control for an isolated steam turbine. In complex operations, optimal tuning plays an imperative role in maintaining the product quality and process safety. This study focuses on the comparison of the optimal PID tuning using Multi-objective Genetic Algorithm (NSGA-II) against normal genetic algorithm and Ziegler Nichols methods for the speed control of an isolated steam turbine. Isolated steam turbine not being connected to the grid; hence is usually used in refineries as steam turbine, where a hydraulic governor is used for the speed control. The PID controller for the system has been designed and implemented using MATLAB and SIMULINK and the results of the design methods have been compared, analysed and conclusions indicates that the significant improvement of results have been obtained by the Multi-Objective GA based optimization of PID as much faster response is obtained as compared to the ordinary GA and Ziegler Nichols method.

Keywords:

Genetic algorithms, isolated steam turbine, multi-objective optimization, PID controllers,

References

1. �str�m, K.J. and T. H�gglund, 2001. The future of PID control. Control Eng. Pract., 9(11): 1163-1175.
   CrossRef    
2. Basu, M. and C. Samiran, 2012. Modeling of steam turbine and its governor of a thermal power plant. J. Inst. Eng. (India): Ser. C, 93(1): 115-121.
   
3. Corriou, J.P., 2004. Process Control: Theory and Applications. Springer, London, pp: 132-133.
   CrossRef    
4. Das, S., I. Pan, S. Das and A. Gupta, 2011. A novel fractional order fuzzy PID controller and its optimal time domain tuning based on integral performance indices. Eng. Appl. Artif. Intel., 25(2): 430-442.
   CrossRef    
5. Goodwin, G.C., S.F. Graebe and M.E. Salgado, 2001. Control System Design. Prentice Hall Inc., New Jersey.
   
6. Ismail, M.M., 2012. Adaptation of PID controller using AI technique for speed control of isolated steam turbine. Proceeding of IEEE Japan-Egypt Conference on Electronics, Communications and Computers (JEC-ECC), pp: 85-90.
   CrossRef    
7. Krohling, R.A. and J.P. Rey, 2001. Design of optimal disturbance rejection PID controllers using genetic algorithms. IEEE T. Evolut. Comput., 5(1): 78-82.
   CrossRef    
8. Murty, M.S.R., year. Governing System: Overview.
   Direct Link
9. Nise, N.S., 2004. Control System Engineering. 4th Edn., John Wiley and Sons, New York.
   PMCid:PMC1740804    
10. Zhao, S.Z., M.W. Iruthayarajan, S. Baskar and P.N. Suganthan, 2011. Multi-objective robust PID controller tuning using two lbests multi-objective particle swarm optimization. Inform. Sci., 181(16): 3323-3335.
    CrossRef    
11. Ziegler, J.G. and N.B. Nichols, 1942. Optimum settings for automatic controllers. T. ASME, 64: 759-768.
    

Competing interests

The authors have no competing interests.

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The authors have no competing interests.