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Design of differential evolution algorithm-based robust fuzzy logic power system stabiliser using minimum rule base

Design of differential evolution algorithm-based robust fuzzy logic power system stabiliser using minimum rule base

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Power system stabiliser must be capable of providing appropriate stabilisation signals over a broad range of operating conditions and disturbances. Literature shows that a fuzzy logic power system stabiliser (FLPSS) is a controller to cover a wider range of operating conditions. Differential evolution algorithm technique is employed in this study to systematically tune the optimal parameters of the proposed fuzzy logic controller. The decisions in fuzzy logic approach are made by forming a series of control rules. The number of control rules to cover all possible combinations of the ‘m’-input variables with ‘n’-membership functions will be of ‘nm’ in number. But more number of rules requires more memory storage and evaluation time. In the proposed work, for a three-input each with three membership functions, the rules are reduced to three rules from 27 rules. The proposed reduced rule-based differential evolution FLPSS not only reduces the memory storage but also reduces the evaluation time. The proposed controller is designed for a linearised model of single machine infinite bus bar system under various operating conditions. The efficacy of the proposed controllers is tested on single machine connected to infinite bus for a three-phase fault and also in multi-machine environment.

References

    1. 1)
      • IEEE recommended Practice for Excitation System Models for Power System stability Studies. IEEE Power Engineering Society, IEEE Std 421.5TM-2005, March 2005 (Revision of IEEE Std 421.5-1992).
    2. 2)
    3. 3)
      • R. Hemmati , S. Mojtaba , S. Boroujeni , M. Abdollahi . Comparison of robust and intelligent based power system stabilizers. Int. J. Phys. Sci. , 17 , 2564 - 2573
    4. 4)
    5. 5)
    6. 6)
      • Yare, Y., Venayagamoorthy, G.K.: `Comparison of DE and PSO for generator maintenance scheduling', 2008 IEEE Swarm Intelligence Symp., 21–23 September 2008, St. Louis, MO, USA.
    7. 7)
      • Z. Kovacic , S. Bogelan . Fuzzy controller design: theory and applications.
    8. 8)
      • Intan, R., Takagi, N.: `Extended concept of logic minimization for rule reduction', IEEE Int. Conf. on Fuzzy Systems, 2010.
    9. 9)
      • Ramirez-Gonzalez, M., Malik, O.P.: `Simplified fuzzy logic controller and its application as a power system stabilizer', 15thInt. Conf. on Intelligent System Applications to Power Systems ISP’09, 2009.
    10. 10)
      • Yu, Y.N., Siggers, C.: `Stabilization and optimal control signals for a power system', Paper 70 TP 531-PWR, At the IEEE Summer Power Meeting and EHV Conf., 12–17 July 1970, Los Angeles, California.
    11. 11)
      • Tomescu, B.: `On the use of fuzzy logic to control paralleled dc-converters', October 2001, , Virginia Polytechnic Institute and State University Blacksburg, Virginia, Dissertation.
    12. 12)
      • T.J. Ross . (1997) Fuzzy logic with engineering applications.
    13. 13)
    14. 14)
      • S.Md. Ayoby , Z. Salam , N.A. Azli . A new optimum design for a single input fuzzy controller applied to DC to AC converters. J. Power Electron. , 3 , 306 - 312
    15. 15)
      • K.R. Padiyar . (2008) Power system dynamics; stability and control.
    16. 16)
      • Sudha, K.R.: `Design of fuzzy logic power system stabilizers: a systematic approach', , , Andhra University, 2006, Dissertation.
    17. 17)
      • K.V. Price , R.N. Storn , J.A. lampinen . Differential evolution: practical approach to global optimization.
    18. 18)
      • J. Machowski , J.W. Bialek , J.R. Bumby . (1997) Power system dynamics and stability.
    19. 19)
      • P. Kundur . (1994) Power system stability and control.
    20. 20)
      • Srikanth, N.V., Vinod Kumar, D.M.: `Investigation of stability of fuzzy logic based power system stabilizers using phase-plane analysis', National Power systems Conf. (NPSC), 2004.
    21. 21)
    22. 22)
    23. 23)
      • Andreoiu, A., Battacharya, K.: `Genetic algorithm based tuning of PID power system stabilizers', Paper 5. 14th PSCC, 24–28 June 2002, Sevilla.
    24. 24)
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