IJISA Vol. 7, No. 1, 8 Dec. 2014
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Small Signal Stability SMIB (single machine on infinite bus bar), SVC (Static Var Compensator), Particle Swarm Optimization (PSO), PSO with Shrinkage Factor & Inertia Weight Approach (PSO-SFIWA), PSO with Time Varying Acceleration Coefficients (PSO-TVAC)
The interconnected systems is continually increasing in size and extending over whole geographical regions, it is becoming increasingly more difficult to maintain synchronism between various parts of the power system. This paper work presents an advanced adaptive Particle swarm optimization technique to optimize the SVC controller parameters for enhancement of the steady state stability & overcoming the premature convergence & stagnation problems as in basic PSO algorithm & Particle swarm optimization with shrinkage factor & inertia weight approach (PSO-SFIWA). In this paper SMIB system along with PID damped SVC controller is considered for study. The generator speed deviation is used as an auxiliary signal to SVC, to generate the desired damping. This controller improves the dynamic performance of power system by reducing the steady-state error. The controller parameters are optimized using basic PSO, PSO-SFIWA & Advanced Adaptive PSO. Computational results show that Advanced Adaptive based SVC controller is able to find better quality solution as compare to conventional PSO & PSO-SFIWA Techniques.
Poonam Singhal, S. K. Agarwal, Narendra Kumar, "Advanced Adaptive Particle Swarm Optimization based SVC Controller for Power System Stability", International Journal of Intelligent Systems and Applications(IJISA), vol.7, no.1, pp.101-110, 2015. DOI:10.5815/ijisa.2015.01.10
[1]N. G. Hingorani, L. Gyugyi, “Understanding FACTS”, IEEE Press, 2001.
[2]P. Kundur, “Power System Stability and Control”, Tata McGraw-Hill, 2006.
[3]K. R. Padiyar, Power System Dynamics Stability and Control, BS Publications, 2nd Edition, Hyderabad, India, 2002
[4]Y. H. Song, A. T. Johns, “Flexible AC Transmission Systems (FACTS)”, IET, 2009
[5]Poonam Singhal, S.K.Aggarwal and Narender Kumar, ‘Transient Stability Enhancement using UPFC.’IREMOS, 2013.
[6]Poonam Singhal, S.K.Agarwal, Narender Kumar & Monika,”Stability Enhancement of a multi-machine power system using Static Var Compensator”IJAIR, volume 2, issue 2,February 2013.
[7]Y. Mishra,S. Mishra and Fangxing Li ‘Coordinated Tuning of DFIG-Based Wind Turbines and Batteries Using Bacteria Foraging Technique for Maintaining Constant Grid Power Output’. Published in Systems Journal, Volume:6 , Issue: 1,pp.no.16-26, 2012 IEEE
[8]Shengli Song Li Kong and Jingjing Cheng,”A Novel Particle Swarm Optimization model with Centroid and its application” I.J. Intelligent Systems and Applications, 2009, 1, pp.no. 42-49
[9]X.Wang, X. Z. Gao and S. J. Ovaska’.”A Hybrid Particle Swarm Optimization Method”, IEEE International Conference on Systems, Man, and Cybernetics, Taipei, Taiwan, October 2006
[10]Mohamed Zellagui and Abdelaziz Chaghi,”Impact of TCSC on Distance Protection Setting based Modified Particle Swarm Optimization Techniques”. I.J. Intelligent Systems and applications 2013,06, pp.no.12-24.
[11]P. S. Shelokar, V. K. Jayaraman, and B. D. Kulkarni,’An ant colony approach for clustering,’ Analytica Chimica Acta Vol. 509(2): pp.187-195, 2004
[12]Shah-Hosseini, H. (2009) ‘The intelligent water drops algorithm: a nature-inspired swarm-based optimisation algorithm’, Int. J.Bio-Inspired Computation, Vol. 1, Nos. 1/2, pp.71–79.
[13]Wang Meihong, Zeng Wenhua*, Wu Keqing. Grid task scheduling based on advanced no velocity PSO’.
[14]Herby Dallard and Sarah S. Lam ‘Solving the Orienteering Problem Using Attractive and Repulsive Particle Swarm Optimization’, Proceedings in IEEE International Conference on Information Reuse and Integration, IRI 2007, pp. no. 13-15, Las Vegas, Nevada, USA. Heuristic Optimization and Search
[15]Rehab F. Abdel Kader,” Genetically Improved PSO Algorithm for Efficient Data Clustering”, Second International Conference on Machine Learning and Computing ( icmlc), pp.71-75, 2010.
[16]Wael M. Korani, Hassen Taher Dorrah, Hassan M.Emara,“Bacterial Foraging Oriented by Particle Swarm Optimization strategy for PID Tuning”.
[17]Jiao Wei, Liu Guang-bin,” An improved Particle Swarm Optimization Algorithm with Immunity”. Published in Intelligent Computation Technology and Automation, 2009. ICICTA '09. Second International Conference on 10-11 Oct. 2009.
[18]Hardiansyah, Junaidi, Yohannes Load Dispatch using Bacterial Foraging Technique with Particle Swarm Optimization Biased Evolution”. I.J. Intelligent Systems and applications 2012,12, pp. 12-18
[19]JinJin-Zhu Hu,Jia-qiao Wang, Ting Xu,” Research on Particle Swarm Optimization with dynamic inertia weight’. Published in 2010 International Conference on Computer Design and Applications (ICCDA 2010).
[20]D.Harikrishna, N.V.Srikanth,”Unified Phillips-Heffron Model of Multi Machine Power System equipped with PID Damping Controlled SVC for Power Oscillation Damping.” Published in India Conference (INDICON), 2009 Annual IEEE, 18-20 Dec. 2009
[21]J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proc .IEEE Int. Conf. Neural Networks, 1995, pp. 1942–1948.
[22]Y.Shi and Russel Eberhart,” Comparing Inertia Weights and Constriction Factors in Particle Swarm Optimization”. Published in: Evolutionary Computation, Proceedings of the 2001
[23]IEEE special stability controls working group, Static VAR compensator models for power flow and Dynamic performance simulation. IEEE Transaction on power systems, Vol. 19, No. 1, February 1994, pp. 229 – 239.
[24]Clerc, “The swarm and the queen: Toward a deterministic and adaptive particle swarm optimization,” in Proc. IEEE Int. Congr .Evolutionary Computation, vol. 3, 1999, p. 1957.
[25]M. Clerc and J.Kennedy, “The particle swarm—Explosion, stability, and convergence in a multi-dimensional complex space,” IEEE Trans. Evol .Comput., vol. 6, pp. 58–73, Feb. 2002.
[26]Asanga Ratanvweera, Saman K. Halgamuge,Harry C.watson, “Self Organising Hierarchical Particle Swarm Optimizater with Time-Varying Acceleration Coefficient.”IEEE Transaction on Evolutionary Computation, 2004
[27]Rajendraprasad Narne and Prafulla Chandra Panda,”Coordinated Design PSS of withMultiple FACTS Controllers using Advanced Adaptive PSO”. International Journal on Electrical Engineering and Informatics ‐ Volume 5,Number 3, September 2013.