{"title":"Optimal Design of Multimachine Power System Stabilizers Using Improved Multi-Objective Particle Swarm Optimization Algorithm","authors":"Badr M. Alshammari, T. Guesmi","volume":120,"journal":"International Journal of Energy and Power Engineering","pagesStart":1438,"pagesEnd":1445,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10005815","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.<\/p>\r\n","references":"[1]\tP. Kundur, Power System Stability and Control, McGraw-Hill, 1994.\r\n[2]\tM. Kashki, Y. L. Abdel-Magid, and M. A. Abido, \u201cParameter optimization of multimachine power system conventional stabilizers using CDCARLA method,\u201d Electrical Power and Energy Systems, vol. 32, pp. 498-506, 2010.\r\n[3]\tD. R. Ostojic, \u201cStabilization of multimodal electromechanical oscillations by coordinated application of power system stabilizers.\u201d IEEE Trans Power Syst, vol. 6, no. 4, pp. 1439-1445, 1991.\r\n[4]\tC. T. Tse, K. W Wang, C. Y. Chung, and K. M. Tsang, \u201cRobust PSS design by probabilistic eigenvalue sensitivity analysis.\u201d Electr Power Syst Res, vol. 59, no. 1, pp. 47-54, 2001.\r\n[5]\tM. A. Abido, \u201cRobust design of multimachine power system stabilizers using simulated annealing.\u201d IEEE Trans Energy Conv, vol. 15, no. 3, pp. 297-304, 2000.\r\n[6]\tH. Werner, P. Korba, and C. T. Yang, \u201cRobust Tuning of Power System Stabilizers Using LMI-Techniques,\u201d IEEE Trans on Control Syst Technol, vol. 11, no. 1, pp. 147\u2013152, 2003.\r\n[7]\tM. Ataei, R. A. Hooshmand, and M. Parastegari, \u201cA wide range robust PSS design based on power system pole-placement using linear matrix inequality.\u201d J Electr Eng, vol. 63, no. 4, pp. 233\u2013241, 2012.\r\n[8]\tS. J. Kim, S. Kwon, and Y. H. Moon, \u201cLow-order robust power system stabilizer for single-machine systems: An LMI approach.\u201d Int J Control Automat and Syst, vol. 8, no. 3, pp. 556-563, 2010.\r\n[9]\tK. Sebaa, and M. Boudour, \u201cOptimal locations and tuning of robust power system stabilizer using genetic algorithms\u201d Int J Electr Power Syst Res, vol. 79, no. 2, pp. 406\u2013416, 2009.\r\n[10]\tL. H. Hassana, M. Moghavvemi, H. A. F. Almurib, K. M. Muttaqi, and V. G. Ganapathy, \u201cOptimization of power system stabilizers using participation factor and genetic algorithm,\u201d Electr Power and Energy Syst, vol. 55, pp. 668\u2013679, 2014.\r\n[11]\tD. K. Sambariya, R. Gupta, and R. Prasad, \u201cDesign of optimal input\u2013output scaling factors based fuzzy PSS using bat algorithm,\u201d Engineering Science and Technology, an International Journal, vol. 19, pp. 991\u20131002, 2016.\r\n[12]\tM. M. Beno, N. A. Singh, M. C. Therase, and M. M. S. Ibrahim, \u201cDesign of PSS for damping low frequency oscillations using bacteria foraging tuned non-linear neuro-fuzzy controller,\u201d Proc. of the IEEE GCC conference and exhibition, 2011, 653\u201356.\r\n[13]\tS. Mishra, M. Tripathy, and J. Nanda, \u201cMultimachine power system stabilizer design by rule based bacteria foraging,\u201d Int J Electr Power Syst Res, vol. 77, no. 12, pp. 1595\u20131607, 2007.\r\n[14]\tB. Zhao, and Y. J. Cao, \u201cMultiple objective particle swarm optimization technique for economic load dispatch,\u201d Journal of Zhejiang University Science, vol. 6A, no. 5, pp. 420 \u2013 427, 2005.\r\n[15]\tB. M. Alshammari, \u201cDynamic Environmental\/Economic Power Dispatch with Prohibited Zones Using Improved Multi-Objective PSO Algorithm,\u201d International Review of Electrical Engineering, vol. 11, no. 4, 2016.\r\n[16]\tJ. Kennedy, and R. Eberhart, \u201cParticle swarm optimization,\u201d Proc. IEEE Int Conference on Neural Networks, pp. 1942 \u2013 1948, 1995.\r\n[17]\tM. A. Abido, \u201cOptimal Design of Power\u2013System Stabilizers Using Particle Swarm Optimization,\u201d IEEE Trans on Energy Conversion, vol. 17, no. 3, 2002.\r\n[18]\tS. Panda, and N. P. Padhy, \u201cRobust Power System Stabilizer Design using Particle Swarm Optimization Technique,\u201d International Journal of Electrical and Computer Engineering, vol. 3, no. 13, 2008.\r\n[19]\tK. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, \u201cA fast and elitist multiobjective genetic algorithm: NSGA-II,\u201d IEEE Trans. on Evolutionary Computation, vol. 6 no. 2, 2002, pp. 182 \u2013 197.\r\n[20]\tC. S. Tsou, S. Chang, and P. Lai, \u201cUsing crowding distance to improve multi-objective PSO with local search,\u201d In-Tech Education and Publishing, 2007.\r\n[21]\tM. Reyes-Sierra, and C. A. C. Coello, \u201cMultiobjective particle swarm optimizers: a survey of the state-of-the-art,\u201d International Journal of Computational Intelligence Research, vol. 2 n0. 3, pp. 287 \u2013 308, 2006.\r\n[22]\tP. M. Anderson, and A. A. Fouad, \u201cPower system control and stability,\u201d Iowa, USA: Iowa State University Press, 1997.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 120, 2016"}