Jump to ContentJump to Main Navigation
Show Summary Details

International Journal of Nonlinear Sciences and Numerical Simulation

Editor-in-Chief: Birnir, Björn

Editorial Board Member: Armbruster, Dieter / Bessaih, Hakima / Chou, Tom / Grauer, Rainer / Marzocchella, Antonio / Rangarajan, Govindan / Trivisa, Konstantina / Weikard, Rudi

8 Issues per year


IMPACT FACTOR 2015: 0.687

SCImago Journal Rank (SJR) 2015: 0.298
Source Normalized Impact per Paper (SNIP) 2015: 0.476
Impact per Publication (IPP) 2015: 0.677

Mathematical Citation Quotient (MCQ) 2015: 0.04

Online
ISSN
2191-0294
See all formats and pricing
Volume 17, Issue 1 (Feb 2016)

Issues

Application of Bat Algorithm to Optimize Scaling Factors of Fuzzy Logic-Based Power System Stabilizer for Multimachine Power System

D. K. Sambariya
  • Corresponding author
  • Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, India
  • Email:
/ R. Prasad
  • Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, India
  • Email:
Published Online: 2016-01-21 | DOI: https://doi.org/10.1515/ijnsns-2015-0025

Abstract

This article presents the design of optimized fuzzy logic-based power system stabilizer (FPSS) to enhance small signal stability using bat algorithm (BA). The proposed optimization of scaling factors of FPSS is considered with an objective function based on square error minimization to guarantee the stability of nonlinear models of test system using BA. The BA-optimized FPSS (BAFPSS) controller is applied to the standard IEEE ten-machine thirty-nine-bus test power system model in the decentralized manner, and the performance is compared with the robust fuzzy controller. The robustness is tested by considering four different models of the test power system with different fault locations to establish the superiority of the proposed BAFPSS over the FPSS.

Keywords: fuzzy power system stabilizer; bat algorithm; BA-optimized fuzzy power system stabilizer; IEEE ten-machine thirty-nine-bus test power system

MSC® (2010).: 70K20; 74Pxx; 78M50; 93Dxx; 93C83

References

  • [1] M. Ramirez-Gonzalez and O. P. Malik, Self-tuned power system stabilizer based on a simple fuzzy logic controller, Electr. Power Compon. Syst. 38 (2010), 407–423, 2010/02/26,doi: [Crossref].

  • [2] F. P. Demello and C. Concordia, Concepts of synchronous machine stability as affected by excitation control, IEEE Trans. Power Apparatus Syst. 88 (1969), 316–329. doi: [Crossref].

  • [3] D. K. Sambariya and R. Gupta, Fuzzy applications in a multi-machine power system stabilizer, J. Electr. Eng. Technol. 5 (2010), 503–510. http://www.jeet.or.kr/ltkpsweb/pub/pubfpfile.aspx?ppseq=100.

  • [4] Y. Zhang, G. P. Chen, O. P. Malik and G. S. Hope, An artificial neural network based adaptive power system stabilizer, IEEE Trans. Energy Convers. 8 (1993), 71–77. doi: [Crossref].

  • [5] L. Guan, S. Cheng and R. Zhou, Artificial neural network power system stabiliser trained with an improved BP algorithm, IEE Proc. Gener. Transm. Distrib. 143 (1996), 135–141. doi: [Crossref].

  • [6] M. Soliman, A. L. Elshafei, F. Bendary, and W. Mansour, LMI static output-feedback design of fuzzy power system stabilizers, Expert Syst. Appl. 36 (2009), 6817–6825. doi: [Crossref]. [Web of Science]

  • [7] V. Mukherjee and S. P. Ghoshal, Intelligent particle swarm optimized fuzzy PID controller for AVR system, Electr. Power Syst. Res. 77 (2007), 1689–1698. doi: [Crossref]. [Web of Science]

  • [8] T.-P. Hong and C.-Y. Lee, Induction of fuzzy rules and membership functions from training examples, Fuzzy Sets Syst. 84 (1996), 33–47. doi: [Crossref].

  • [9] D. K. Sambariya, R. Gupta and A. K. Sharma, Fuzzy applications to single machine power system stabilizers, J. Theor. Appl. Inf. Technol. 5 (2009), 317–324. http://www.jatit.org/volumes/research-papers/Vol5No3/9Vol5No3.pdf

  • [10] D. K. Sambariya and R. Prasad, Robust power system stabilizer design for single machine infinite bus system with different membership functions for fuzzy logic controller, Proceedings of 7th International Conference on Intelligent Systems and Control (ISCO 2013), at Korpagam College of Engineering, Coimbatore, Tamilnadu, India, vol. IEEE Catalog number (Print): CFP1387T-PRT, ISBN (Print): 978-1-4673-4601-6, 13–19, January 4–5, 2013. doi: [Crossref].

  • [11] D. K. Sambariya and R. Prasad, Optimal tuning of fuzzy logic power system stabilizer using harmony search algorithm, Int. J. Fuzzy Syst. (2015), 1–14. doi: [Crossref].

  • [12] D. K. Sambariya, Power system stabilizer design using compressed rule base of fuzzy logic controller, J. Electr. Electron. Eng. 3 (July 1, 2015), 52–64. doi: [Crossref].

  • [13] D. K. Sambariya and R. Prasad, Power system stabilizer design for multimachine power system using interval type-2 fuzzy logic controller, Int. Rev. Electr. Eng. 8 (October 2013), 1556–1565. doi: [Crossref].

  • [14] D. K. Sambariya and R. Prasad, Evaluation of interval type-2 fuzzy membership function & robust design of power system stabilizer for SMIB power system, Sylwan J. 158 (April 2014), 289–307. http://sylwan.ibles.org/archive.php?v=158&i=5.

  • [15] H. Alkhatib and J. Duveau, Dynamic genetic algorithms for robust design of multimachine power system stabilizers, Int. J. Electr. Power Energy Syst. 45 (2013), 242–251. doi: [Crossref]. [Web of Science]

  • [16] A. Al-Hinai, Dynamic Stability Enhancement Using Genetic Algorithm Power System Stabilizer, in International Conference on Power System Technology (POWERCON), 2010, 1–7. doi: [Crossref].

  • [17] R. Gupta, B. Bandyopadhyay and A. M. Kulkarni, Robust decentralized fast-output sampling technique based power system stabilizer for a multi-machine power system, Int. J. Syst. Sci. 36 (2005), 297–314, 2005/04/15. doi: [Crossref].

  • [18] P. Idowu and A. Ghandakly, A discrete-time coordinated adaptive stabilizer for multimachine power systems, Int. J. Syst. Sci. 27 (1996), 597–604, 1996/07/01. doi: [Crossref].

  • [19] D. Gan, Z. QU, and H. CAI, multi machine power system excitation control design via theories of feedback linearization control and nonlinear robust control, Int. J. Syst. Sci. 31 (2000), 519–527.

  • [20] E. Abu-Al-Feilat, M. Bettayeb, H. Al-Duwaish, M. Abido and A. Mantawy, A neural network-based approach for on-line dynamic stability assessment using synchronizing and damping torque coefficients, Electr. Power Syst. Res. 39 (1996), 103–110. doi: [Crossref].

  • [21] S. M. Abd-Elazim and E. S. Ali, A hybrid particle swarm optimization and bacterial foraging for optimal power system stabilizers design, Int. J. Electr. Power Energy Syst. 46 (2013), 334–341. doi: [Crossref]. [Web of Science]

  • [22] H. M. Soliman, E. H. E. Bayoumi and M. F. Hassan, PSO–based power system stabilizer for minimal overshoot and control constraints, J. Electr. Eng. 59 (2008), 153–159. http://iris.elf.stuba.sk/JEEEC/data/pdf/3_108-6.

  • [23] M. A. Abido, Robust design of power system stabilizers for multimachine power systems using differential evolution, in: Computational Intelligence in Power Engineering, pp. 1–18. vol. 302, B. Panigrahi, A. Abraham and S. Das (eds.). Springer Berlin, Heidelberg, 2010. doi: [Crossref].

  • [24] Z. W. Geem, Harmony search applications in industry, in: Studies in Fuzziness and Soft Computing, Volume 226, Soft Computing Applications in Industry, pp. 117–134. vol. 226, B. Prasad (ed.). Springer-Verlag, Berlin and Heidelberg, 2008. doi: [Crossref].

  • [25] E. Alfaro-Cid, E. W. McGookin, and D. J. Murray-Smith, Optimisation of the weighting functions of an H ∞ controller using genetic algorithms and structured genetic algorithms, Int. J. Syst. Sci. 39 (2008), 335–347, 2008/04/01. doi: [Crossref]. [Web of Science]

  • [26] D. K. Sambariya and R. Prasad, Design of harmony search algorithm based tuned fuzzy logic power system stabilizer, Int. Rev. Electr. Eng. 8 (October 2013), 1594–1607. doi: [Crossref].

  • [27] M. A. Abido, Simulated annealing based approach to PSS and FACTS based stabilizer tuning, Int. J. Electr. Power Energy Syst. 22 (2000), 247–258. doi: [Crossref].

  • [28] X. S. Yang, A new metaheuristic bat-inspired algorithm, in: Nature inspired cooperative strategies for optimization (NICSO 2010), pp. 65–74, vol. 284, J. González, D. Pelta, C. Cruz, G. Terrazas and N. Krasnogor (eds.). Springer Berlin, Heidelberg, 2010. doi: [Crossref].

  • [29] D. K. Sambariya and R. Prasad, Robust tuning of power system stabilizer for small signal stability enhancement using metaheuristic bat algorithm, Int. J. Electr. Power Energy Syst. 61 (2014), 229–238. doi: [Crossref] [Web of Science]

  • [30] Y.-Y. Hsu and C.-L. Chen, Identification of optimum location for stabiliser applications using participation factors, IEE Proc. Gener. Transm. Distrib. 134 (1987), 238–244. doi: [Crossref].

  • [31] M. A. Abido, Optimal design of power-system stabilizers using particle swarm optimization, IEEE Trans. Energy Convers. 17 (2002), 406–413. doi: [Crossref].

  • [32] A. Mahabuba and M. Abdullah Khan, Optimal location of power system stabilizers in a multi machine power system using relative gain array (RGA) and genetic algorithm (GA), Int. J. Electr. Power Eng. 2 (2008), 19–27.

  • [33] S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, Optimization by simulated annealing, Science 220 (13 May 1983). doi: [Crossref].

  • [34] Y. Zhang and A. Bose, Design of wide-area damping controllers for interarea oscillations, IEEE Trans. Power Syst. 23 (2008), 1136–1143. doi: [Crossref]. [Web of Science]

  • [35] M. A. Pai, Energy function analysis for power system stability, K1uwer Academic Publishers, Norwell, MA, 1989.

  • [36] P. W. Sauer and M. A. Pai, Power system dynamics and stability, Prentice-Hall of India Pvt. Limited, New Delhi, 1998.

About the article

Received: 2015-03-04

Accepted: 2015-12-30

Published Online: 2016-01-21

Published in Print: 2016-02-01


Citation Information: International Journal of Nonlinear Sciences and Numerical Simulation, ISSN (Online) 2191-0294, ISSN (Print) 1565-1339, DOI: https://doi.org/10.1515/ijnsns-2015-0025. Export Citation

Comments (0)

Please log in or register to comment.
Log in