Optimal Design of Multimachine Power System Stabilizers Using Improved Multi-Objective Particle Swarm Optimization Algorithm
In this paper, the concept of a non-dominated sorting multi-objective particle swarm optimization with local search (NSPSO-LS) is presented for the optimal design of multimachine power system stabilizers (PSSs). The controller design is formulated as an optimization problem in order to shift the system electromechanical modes in a pre-specified region in the s-plan. A composite set of objective functions comprising the damping factor and the damping ratio of the undamped and lightly damped electromechanical modes is considered. The performance of the proposed optimization algorithm is verified for the 3-machine 9-bus system. Simulation results based on eigenvalue analysis and nonlinear time-domain simulation show the potential and superiority of the NSPSO-LS algorithm in tuning PSSs over a wide range of loading conditions and large disturbance compared to the classic PSO technique and genetic algorithms.
[1] P. Kundur, Power System Stability and Control, McGraw-Hill, 1994.
[2] M. Kashki, Y. L. Abdel-Magid, and M. A. Abido, “Parameter optimization of multimachine power system conventional stabilizers using CDCARLA method,” Electrical Power and Energy Systems, vol. 32, pp. 498-506, 2010.
[3] D. R. Ostojic, “Stabilization of multimodal electromechanical oscillations by coordinated application of power system stabilizers.” IEEE Trans Power Syst, vol. 6, no. 4, pp. 1439-1445, 1991.
[4] C. T. Tse, K. W Wang, C. Y. Chung, and K. M. Tsang, “Robust PSS design by probabilistic eigenvalue sensitivity analysis.” Electr Power Syst Res, vol. 59, no. 1, pp. 47-54, 2001.
[5] M. A. Abido, “Robust design of multimachine power system stabilizers using simulated annealing.” IEEE Trans Energy Conv, vol. 15, no. 3, pp. 297-304, 2000.
[6] H. Werner, P. Korba, and C. T. Yang, “Robust Tuning of Power System Stabilizers Using LMI-Techniques,” IEEE Trans on Control Syst Technol, vol. 11, no. 1, pp. 147–152, 2003.
[7] M. Ataei, R. A. Hooshmand, and M. Parastegari, “A wide range robust PSS design based on power system pole-placement using linear matrix inequality.” J Electr Eng, vol. 63, no. 4, pp. 233–241, 2012.
[8] S. J. Kim, S. Kwon, and Y. H. Moon, “Low-order robust power system stabilizer for single-machine systems: An LMI approach.” Int J Control Automat and Syst, vol. 8, no. 3, pp. 556-563, 2010.
[9] K. Sebaa, and M. Boudour, “Optimal locations and tuning of robust power system stabilizer using genetic algorithms” Int J Electr Power Syst Res, vol. 79, no. 2, pp. 406–416, 2009.
[10] L. H. Hassana, M. Moghavvemi, H. A. F. Almurib, K. M. Muttaqi, and V. G. Ganapathy, “Optimization of power system stabilizers using participation factor and genetic algorithm,” Electr Power and Energy Syst, vol. 55, pp. 668–679, 2014.
[11] D. K. Sambariya, R. Gupta, and R. Prasad, “Design of optimal input–output scaling factors based fuzzy PSS using bat algorithm,” Engineering Science and Technology, an International Journal, vol. 19, pp. 991–1002, 2016.
[12] M. M. Beno, N. A. Singh, M. C. Therase, and M. M. S. Ibrahim, “Design of PSS for damping low frequency oscillations using bacteria foraging tuned non-linear neuro-fuzzy controller,” Proc. of the IEEE GCC conference and exhibition, 2011, 653–56.
[13] S. Mishra, M. Tripathy, and J. Nanda, “Multimachine power system stabilizer design by rule based bacteria foraging,” Int J Electr Power Syst Res, vol. 77, no. 12, pp. 1595–1607, 2007.
[14] B. Zhao, and Y. J. Cao, “Multiple objective particle swarm optimization technique for economic load dispatch,” Journal of Zhejiang University Science, vol. 6A, no. 5, pp. 420 – 427, 2005.
[15] B. M. Alshammari, “Dynamic Environmental/Economic Power Dispatch with Prohibited Zones Using Improved Multi-Objective PSO Algorithm,” International Review of Electrical Engineering, vol. 11, no. 4, 2016.
[16] J. Kennedy, and R. Eberhart, “Particle swarm optimization,” Proc. IEEE Int Conference on Neural Networks, pp. 1942 – 1948, 1995.
[17] M. A. Abido, “Optimal Design of Power–System Stabilizers Using Particle Swarm Optimization,” IEEE Trans on Energy Conversion, vol. 17, no. 3, 2002.
[18] S. Panda, and N. P. Padhy, “Robust Power System Stabilizer Design using Particle Swarm Optimization Technique,” International Journal of Electrical and Computer Engineering, vol. 3, no. 13, 2008.
[19] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Trans. on Evolutionary Computation, vol. 6 no. 2, 2002, pp. 182 – 197.
[20] C. S. Tsou, S. Chang, and P. Lai, “Using crowding distance to improve multi-objective PSO with local search,” In-Tech Education and Publishing, 2007.
[21] M. Reyes-Sierra, and C. A. C. Coello, “Multiobjective particle swarm optimizers: a survey of the state-of-the-art,” International Journal of Computational Intelligence Research, vol. 2 n0. 3, pp. 287 – 308, 2006.
[22] P. M. Anderson, and A. A. Fouad, “Power system control and stability,” Iowa, USA: Iowa State University Press, 1997.
[1] P. Kundur, Power System Stability and Control, McGraw-Hill, 1994.
[2] M. Kashki, Y. L. Abdel-Magid, and M. A. Abido, “Parameter optimization of multimachine power system conventional stabilizers using CDCARLA method,” Electrical Power and Energy Systems, vol. 32, pp. 498-506, 2010.
[3] D. R. Ostojic, “Stabilization of multimodal electromechanical oscillations by coordinated application of power system stabilizers.” IEEE Trans Power Syst, vol. 6, no. 4, pp. 1439-1445, 1991.
[4] C. T. Tse, K. W Wang, C. Y. Chung, and K. M. Tsang, “Robust PSS design by probabilistic eigenvalue sensitivity analysis.” Electr Power Syst Res, vol. 59, no. 1, pp. 47-54, 2001.
[5] M. A. Abido, “Robust design of multimachine power system stabilizers using simulated annealing.” IEEE Trans Energy Conv, vol. 15, no. 3, pp. 297-304, 2000.
[6] H. Werner, P. Korba, and C. T. Yang, “Robust Tuning of Power System Stabilizers Using LMI-Techniques,” IEEE Trans on Control Syst Technol, vol. 11, no. 1, pp. 147–152, 2003.
[7] M. Ataei, R. A. Hooshmand, and M. Parastegari, “A wide range robust PSS design based on power system pole-placement using linear matrix inequality.” J Electr Eng, vol. 63, no. 4, pp. 233–241, 2012.
[8] S. J. Kim, S. Kwon, and Y. H. Moon, “Low-order robust power system stabilizer for single-machine systems: An LMI approach.” Int J Control Automat and Syst, vol. 8, no. 3, pp. 556-563, 2010.
[9] K. Sebaa, and M. Boudour, “Optimal locations and tuning of robust power system stabilizer using genetic algorithms” Int J Electr Power Syst Res, vol. 79, no. 2, pp. 406–416, 2009.
[10] L. H. Hassana, M. Moghavvemi, H. A. F. Almurib, K. M. Muttaqi, and V. G. Ganapathy, “Optimization of power system stabilizers using participation factor and genetic algorithm,” Electr Power and Energy Syst, vol. 55, pp. 668–679, 2014.
[11] D. K. Sambariya, R. Gupta, and R. Prasad, “Design of optimal input–output scaling factors based fuzzy PSS using bat algorithm,” Engineering Science and Technology, an International Journal, vol. 19, pp. 991–1002, 2016.
[12] M. M. Beno, N. A. Singh, M. C. Therase, and M. M. S. Ibrahim, “Design of PSS for damping low frequency oscillations using bacteria foraging tuned non-linear neuro-fuzzy controller,” Proc. of the IEEE GCC conference and exhibition, 2011, 653–56.
[13] S. Mishra, M. Tripathy, and J. Nanda, “Multimachine power system stabilizer design by rule based bacteria foraging,” Int J Electr Power Syst Res, vol. 77, no. 12, pp. 1595–1607, 2007.
[14] B. Zhao, and Y. J. Cao, “Multiple objective particle swarm optimization technique for economic load dispatch,” Journal of Zhejiang University Science, vol. 6A, no. 5, pp. 420 – 427, 2005.
[15] B. M. Alshammari, “Dynamic Environmental/Economic Power Dispatch with Prohibited Zones Using Improved Multi-Objective PSO Algorithm,” International Review of Electrical Engineering, vol. 11, no. 4, 2016.
[16] J. Kennedy, and R. Eberhart, “Particle swarm optimization,” Proc. IEEE Int Conference on Neural Networks, pp. 1942 – 1948, 1995.
[17] M. A. Abido, “Optimal Design of Power–System Stabilizers Using Particle Swarm Optimization,” IEEE Trans on Energy Conversion, vol. 17, no. 3, 2002.
[18] S. Panda, and N. P. Padhy, “Robust Power System Stabilizer Design using Particle Swarm Optimization Technique,” International Journal of Electrical and Computer Engineering, vol. 3, no. 13, 2008.
[19] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Trans. on Evolutionary Computation, vol. 6 no. 2, 2002, pp. 182 – 197.
[20] C. S. Tsou, S. Chang, and P. Lai, “Using crowding distance to improve multi-objective PSO with local search,” In-Tech Education and Publishing, 2007.
[21] M. Reyes-Sierra, and C. A. C. Coello, “Multiobjective particle swarm optimizers: a survey of the state-of-the-art,” International Journal of Computational Intelligence Research, vol. 2 n0. 3, pp. 287 – 308, 2006.
[22] P. M. Anderson, and A. A. Fouad, “Power system control and stability,” Iowa, USA: Iowa State University Press, 1997.
@article{"International Journal of Electrical, Electronic and Communication Sciences:74277", author = "Badr M. Alshammari and T. Guesmi", title = "Optimal Design of Multimachine Power System Stabilizers Using Improved Multi-Objective Particle Swarm Optimization Algorithm", abstract = "In this paper, the concept of a non-dominated sorting multi-objective particle swarm optimization with local search (NSPSO-LS) is presented for the optimal design of multimachine power system stabilizers (PSSs). The controller design is formulated as an optimization problem in order to shift the system electromechanical modes in a pre-specified region in the s-plan. A composite set of objective functions comprising the damping factor and the damping ratio of the undamped and lightly damped electromechanical modes is considered. The performance of the proposed optimization algorithm is verified for the 3-machine 9-bus system. Simulation results based on eigenvalue analysis and nonlinear time-domain simulation show the potential and superiority of the NSPSO-LS algorithm in tuning PSSs over a wide range of loading conditions and large disturbance compared to the classic PSO technique and genetic algorithms.
", keywords = "Multi-objective optimization, particle swarm optimization, power system stabilizer, low frequency oscillations. ", volume = "10", number = "12", pages = "1438-7", }
