Gowrishankar KASILINGAM, Jagadeesh PASUPULETI

BBO algorithm-based tuning of PID controller for speed control of synchronous machine

A biogeography-based optimization (BBO) algorithm was used for tuning the parameters of a proportional integral derivative (PID) controller-based power system stabilizer (PSS). The proposed method minimizes the low frequency electromechanical oscillations (0.1 2.5 Hz) and enhances the stability of the power system by optimally tuning the PID parameters. This was achieved by minimizing the objective function of the integral square error for various disturbances. The performance of the BBO algorithm was tested on a single machine infinite bus system for a different range of operating conditions and the results were compared with particle swam optimization, adaptation law, and conventional PSS. The result analysis concluded that the BBO algorithm damps out the low frequency oscillations in the rotor of the synchronous machine effectively when compared to other methods. The algorithms were simulated with MATLAB/Simulink. The results from the simulation showed that the proposed controller yields a fast convergence rate and better dynamic performance.

PDF

___

[1] Bose A, Canizares C, Hatziargyriou N, Kundur P, Stankovic A, Paserba J, Ajjarapu V, Taylor C, Anderson G, Hill D et al. Definition and classification of power system stability. IEEE T Power Syst 2004; 19: 1387-1401.
[2] Kundur P. Power System Stability and Control. 3rd ed. New York, NY, USA: McGraw-Hill, 1993.
[3] Vijayakumar P, Nagaraj B. A comparative study of PID controller tuning using GA, EP, PSO and ACO. J Autom Mobi Robot Int Syst 2011; 5: 42-48.
[4] Gowrishankar K, Ragavendiran R, Gnanadass R. Modelling and performance analysis of proportional integral derivative controller with PSS using Simulink. In: 4th IEEE International Conference on Computing Applications in Electrical Engineering; 2021 February 2010; Roorkee, India. pp. 5-8.
[5] Abido MA, Abdel-Magid YL. Hybridizing rule based power system stabilizers with genetic algorithms. IEEE T Power Syst 1999; 14: 600-607.
[6] Duman S, Ozturk A. Robust design of PID controller for power system stabilization by real coded genetic algorithm. Int Rev Electr Eng 2010; 5: 2159-2170.
[7] Hakim E A, Soeprijanto A. Multi-machine power system stabilizer based on optimal fuzzy PID with genetic algorithm tuning. J Energy Pow Eng 2010; 4: 43-50.
[8] Babaei E, Galvani S, Ahmad Jirdehi M. Design of robust power system stabilizer based on PSO. In: IEEE Symposium on Industrial Electronics and Applications; 47 October 2009; Kuala Lumpur, Malaysia. pp. 325-330.
[9] Mahmoud MS, Soliman HM. Design of robust power system stabilizer based on particle swarm optimization. Circuit Syst 2012; 3: 82-89.
[10] Abido MA. Optimal design of power system stabilizers using particle swarm optimization. IEEE T Energy Conver 2002; 17: 406-413.
[11] Sheeba R, Jayaraju M, Sundareswaran K. Performance enhancement of power system stabilizer through colony of foraging ants. Electr Pow Compo Sys 2014; 42: 1016-1028.
[12] Kennedy J, Eberhart R. Particle swarm optimization. In: IEEE International Conference on Neural Networks; 27 November1 December 1995; Perth, Australia. pp. 1942-1948.
[13] Hsu Y, Wu CJ. Design of self-tuning PID power system stabilizer for multi-machine power system. IEEE T Power Syst 1988; 3: 1059-1064.
[14] Simon DJ. Biogeography based optimization. IEEE T Evolut Comput 2008; 12: 702-713.
[15] Brown JH, Riddle BR, Lomolino MV. Bio-geography. 3rd ed. Sunderland, MA, USA: Sinauer Associates, 2009.
[16] Khelfi MH, Salem M. Application of biogeography based optimization in tuning of PID controller for non-linear systems. In: IEEE International Conference on Complex Systems; 56 November 2012; Agadir, Morocco. pp. 1-6.
[17] Xiang LC, Jiangtao C, Fuli W. Local search strategy biogeography based optimization algorithm for self-tuning of PID parameters. In: 32nd Chinese Control Conference; 2628 July 2013; Xian, China. pp. 4306-4310.
[18] Lakshmi P, Karthikeyan K. Optimal design of PID controller for improving rotor angle stability using BBO. Procedia Engineering 2012; 38: 889-902.
[19] Gowrishankar K. Modelling and performance analysis of PID power system stabilizer using adaptation law in Simulink environment. In: IEEE International Conference on Electrical, Computer and Communication Technologies; 1213 September 2011; Tamil Nadu, India. pp. 83-89.
[20] Gowrishankar K, Jagadeesh P. Tuning of PID controller for a synchronous machine connected to a non linear load. ARPN Journal of Engineering and Applied Sciences 2014; 9: 1659-1668.