Skip to main content
SpringerLink
Log in

A New Cascade Fuzzy Power System Stabilizer for Multi-machine System Stability Enhancement

  • Published:
Journal of Control, Automation and Electrical Systems Aims and scope Submit manuscript

Abstract

This paper introduces a new robust controller with cascaded fuzzy blocks as a power system stabilizer (CFPSS) to enhance damping during low-frequency oscillations. This CFPSS is designed to act as a nonlinear lead–lag PSS with a given number of compensation blocks. To demonstrate the efficiency and the robustness of this proposed stabilizer, simulation results performed on the IEEE three-generator nine-bus multi-machine power system subjected to a three-phase short-circuit fault have been carried out. The parameters of the proposed PSS and those of the conventional IEEE linear lead–lag PSS have been tuned by a recently developed optimization technique (krill herd algorithm). The robustness of this novel CFPSS is proved, by optimizing the parameters of the two PSSs for one operating point (normally loaded system) and applying them to other operating points (case of heavy and low loads) with some key parameters variation. The obtained results have shown the superiority and the robustness of the CFPSS comparatively to the conventional IEEE lead–lag PSS in terms of oscillations damping over a wide range of operating conditions and against parametric variation. The same conclusions have been drawn in the case of a large power system (IEEE 16-machine, 68-bus test system) characterized by its local and inter-area oscillations modes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  • Abido, M. A. (2002). Optimal design of power system stabilizers using particle swarm optimization. IEEE Transactions on Energy Conversion, 17(3), 406–413.

    Article  Google Scholar 

  • Abido, A., & Abdel-Magid, L. (2002). Optimal design of power system stabilizers using evolutionary programming. IEEE Transactions on Energy Conversion, 17(4), 429–436.

    Article  Google Scholar 

  • Aboul-Ela, M. E., Sallam, A. A., McCalley, J. D., & Fouad, A. A. (1996). Damping controller design for power system oscillations using global signals. IEEE Transactions on Power Systems, 11(2), 767–773.

    Article  Google Scholar 

  • Ali, E. S. (2014). Optimization of power system stabilizers using BAT search algorithm. Electrical Power and Energy Systems, 61, 683–690.

    Article  Google Scholar 

  • Anderson, P. M., & Fouad, A. A. (2008). Power system control and stability. New York: Wiley.

    Google Scholar 

  • Boukarim, G. E., Wang, S., Chow, J. H., Taranto, G. N., & Martins, N. (2000). A comparison of classical, robust and decentralized control designs for multiple power system stabilizers. IEEE Transactions on Power Systems, 15(4), 1287–1292.

    Article  Google Scholar 

  • Chatterjee, A., Ghoshal, S. P., & Mukherjee, V. (2011). Chaotic ant swarm optimization for fuzzy-based tuning of power system stabilizer. International Journal of Electrical Power & Energy Systems, 33, 657–672.

    Article  Google Scholar 

  • Chitara, D., Swarnkar, A., Gupta, N., et al. (2015). Optimal tuning of multimachine power system stabilizer using cuckoo search algorithm. IFAC, 48(30), 143–148.

    Google Scholar 

  • Choucha, A., Hellal, A., Mokrani, L., & Arif, S. (2012). New approach to the optimization of power system stabilizers: Genetic algorithm with dynamic constraints. Control and Intelligent Systems, 40(3), 129–143.

    Article  MathSciNet  Google Scholar 

  • De Menezes, M. M., de Araujo, P. B., & do Valle, D. B. (2016). Design of PSS and TCSC damping controller using particle swarm optimization. Journal of Control, Automation and Electrical Systems, 27(5), 554–561.

    Article  Google Scholar 

  • De Oliveira, R. V., Ramos, R. A., & Bretas, N. G. (2010). An algorithm for computerized automatic tuning of power system stabilizers. Control Engineering Practice, 18, 45–54.

    Article  Google Scholar 

  • De Vargas Fortes, E., de Araujo, P. B., & Macedo, L. H. (2016a). Coordinated tuning of the parameters of PI, PSS and POD controllers using a specialized Chu–Beasley’s genetic algorithm. Electric Power Systems Research, 140, 708–721.

    Article  Google Scholar 

  • De Vargas Fortes, E., de Araujo, P. B., Macedo, L. H., Gamino, B. R., & Martins, L. F. B. (2016b). Analysis of the influence of PSS and IPFC-POD controllers in small-signal stability using a simulated annealing algorithm. In 2016 12th IEEE international conference on industry applications (INDUCSON), Curitiba.

  • De Vargas Fortes, E., Macedo, L. H., de Araujo, P. B., & Romero, R. (2018). A VNS algorithm for the design of supplementary damping controllers for small-signal stability analysis. International Journal of Electrical Power & Energy Systems, 94, 41–56.

    Article  Google Scholar 

  • Demello, F. P., & Concordia, C. (1969). Concepts of synchronous machine stability as effected by excitation control. IEEE Transactions on Power Apparatus and Systems, 88(4), 316–329.

    Article  Google Scholar 

  • Eke, I., Taplamacioglu, M. C., & Lee, K. Y. (2015). Robust tuning of power system stabilizer by using orthogonal learning artificial bee colony. IFAC, 48(30), 149–154.

    Google Scholar 

  • Elazim, S. A., & Ali, E. S. (2016). Optimal power system stabilizers design via cuckoo search algorithm. International Journal of Electrical Power Energy System, 75, 99–107.

    Article  Google Scholar 

  • El-Zonkoly, A. M., Khalil, A. A., & Ahmied, N. M. (2009). Optimal tuning of lead–lag and fuzzy logic power system stabilizers using particle swarm optimization. Expert Systems with Applications, 36, 2097–2106.

    Article  Google Scholar 

  • Esmaili, M. R., Khodabakhshian, A., GhaebiPanahc, P., & Azizkhanid, S. (2013). A new robust multi-machine power system stabilizer design using quantitative feedback theory. Procedia Technology, 11(1), 75–85.

    Article  Google Scholar 

  • Feliachi, A., Zhang, X., & Sims, S. C. (1988). Power system stabilizers design using optimal reduced order models. Part II: design. IEEE Transactions on Power Systems, 3(4), 1676–1684.

    Article  Google Scholar 

  • Fraile-Ardanuy, J., & Zufiria, P. J. (2007). Design and comparison of adaptive power system stabilizers based on neural fuzzy networks and genetic algorithms. Neurocomputing, 70(2), 902–912.

    Google Scholar 

  • Gandomi, A., & Alavi, A. (2012). A new bio inspired optimization algorithm. Communications in Nonlinear Science and Numerical Simulation, 17(12), 4831–4845.

    Article  MathSciNet  MATH  Google Scholar 

  • Ghosh, A., Ledwich, G., Malik, O. P., & Hope, G. S. (1984). Power system stabilizer based on adaptive control techniques. IEEE Transactions on Power Apparatus and Systems, 103, 1983–1986.

    Article  Google Scholar 

  • Ghoshal, S. P., Chatterjee, A., & Mukherjee, V. (2009). Bio-inspired fuzzy logic based tuning of power system stabilizer. Expert System Applications, 36(5), 9281–9292.

    Article  Google Scholar 

  • Hardiansyah, F. S., Furuya, S., & Irisawa, J. (2006). A robust H∞ power system stabilizer design using reduced-order models. Electrical Power Energy Systems, 28, 21–28.

    Article  Google Scholar 

  • Jebali, M., Kahouli, O., & Hadj Abdallah, H. (2017). Optimizing PSS parameters for a multi-machine power system using genetic algorithm and neural network techniques. International Journal of Advanced Manufacture and Technology, 90, 2669–2688.

    Article  Google Scholar 

  • Jilledi, S. K. (2017). Improving profile parameters of the power system network using krill heard algorithm with facts device: UPFC. Global Journal of Researches in Engineering, 17(3), 1–13.

    Google Scholar 

  • Kundur, P. (1994). Power system stability and control. New York: McGraw-Hill.

    Google Scholar 

  • Kundur, P., Klein, M., Rogers, G. J., & Zywno, M. S. (1989). Application of power system stabilizers for enhancement of overall system stability. IEEE Transactions on Power Systems, 4(2), 614–626.

    Article  Google Scholar 

  • Kvasov, D., Menniti, D., Pinnarelli, A., Sergeyev, Y., & Sorrentino, N. (2008). Tuning fuzzy power-system stabilizers in multi-machine systems by global optimization algorithms based on efficient domain partitions. Electrical Power System Research, 78(7), 1217–1229.

    Article  Google Scholar 

  • Larsen, E. V., & Swann, D. A. (1981). Applying power system stabilizers: parts I, II and III. IEEE Transactions on Power Apparatus and Systems, 100(6), 3017–3046.

    Article  Google Scholar 

  • Lee, S. S., & Park, J. K. (1998). Design of power system stabilizer using observer/sliding mode, observer/sliding mode model following and H∞/sliding mode controllers for small signal stability study. Electrical Power & Energy Systems, 20(8), 543–553.

    Article  Google Scholar 

  • Li, Q., & Liu, B. (2017). Clustering using an improved krill herd algorithm. MDPI Journal, 10(2), 1–12.

    MathSciNet  MATH  Google Scholar 

  • Lin, Y. J. (2013). Proportional plus derivative output feedback based fuzzy logic power system stabilizer. Electrical Power and Energy Systems, 44(1), 301–307.

    Article  Google Scholar 

  • Mamdani, E. H. (1974). Applications of fuzzy algorithms for control of simple dynamic plant. Proceedings of the IEE Control & Science, 121(12), 1585–1588.

    Google Scholar 

  • Martins, L. F. B., de Araujo, P. B., de Vargas Fortes, E., & Macedo, L. H. (2017). Design of the PI–UPFC–POD and PSS damping controllers using an artificial bee colony algorithm. Journal of Control, Automation and Electrical Systems, 28(6), 762–773.

    Article  Google Scholar 

  • Mekhanet, M., Mokrani, L., Ameur, A., & Attia, Y. (2016). Adaptive fuzzy gain of power system stabilizer to improve the global stability. Bulletin of Electrical Engineering and Informatics, 5(4), 421–429.

    Google Scholar 

  • Miotto, E. L., de Araujo, P. B., Gamino, B. R., Fortes, E. D. V., & Martins, L. F. B. (2016). Coordinated tuning of the parameters of supplementary controllers damping using bio-inspired algorithms. In 2012 12th IEEE international conference on industry applications (INDUCSON), Curitiba.

  • Mishra, S., Tripathy, M., & Nanda, J. (2007). Multi-machine power system stabilizer design by rule based bacteria foraging. Electrical Power System Research, 77, 1595–1607.

    Article  Google Scholar 

  • Pai, M. A., Sen Gupta, D. P., & Padiyar, K. R. (2004). Small signal analysis of power systems (1st ed.). New Delhi: Narosa Publishing House.

    Google Scholar 

  • Panda, S., & Padhy, N. P. (2008). Robust power system stabilizer design using particle swarm optimization technique. International Journal of Electrical and Computer Engineering, 2(10), 2260–2267.

    Google Scholar 

  • Park, Y. M., & Kim, W. (1996). Discrete time adaptive sliding mode power system stabilizer with only input/output measurements. Electrical Power & Energy Systems, 18, 509–517.

    Article  Google Scholar 

  • Ramos, R. A., Alberto, L. F. C., & Bretas, N. G. (2004). A new methodology for the coordinated design of robust decentralized power system damping controllers. IEEE Transactions on Power Systems, 19(1), 444–454.

    Article  Google Scholar 

  • Rogers, G. (2000). Power system oscillations (pp. 314–317). Boston, MA: Kluwer.

    Book  Google Scholar 

  • Sambariya, D. K., Gupta, R., & Prasad, R. (2016). Design of optimal input-output scaling factors based fuzzy pss using bat algorithm. Engineering Science and Technology, an International Journal, 19(2), 991–1002.

    Article  Google Scholar 

  • Sambariya, D. K., & Rajeev, G. (2010). Fuzzy applications in a multi-machine power system stabilizer. Journal of Electrical Engineering & Technology., 5(3), 503–510.

    Article  Google Scholar 

  • Segal, R., Sharma, A., & Kothari, M. L. (2004). A self-tuning power system stabilizer based on artificial neural network. International Journal of Electrical Power Energy System, 26(6), 423–430.

    Article  Google Scholar 

  • Sugeno, M., & Kang, G. T. (1989). Structure identification of fuzzy model. Fuzzy Sets and Systems, 28, 15–33.

    Article  MathSciNet  MATH  Google Scholar 

  • Takagi, T., & Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man and Cybernetics, 15(1), 116–132.

    Article  MATH  Google Scholar 

  • Wang, H., & Du, W. (2016). Analysis and damping control of power system low-frequency. Boston: Springer.

    Book  MATH  Google Scholar 

  • Wenyan, Gu, & Bollinger, K. E. (1989). A self-tuning power system stabilizer for wide-range synchronous generator operation. IEEE Transactions on Power Systems, 4(3), 1191–1199.

    Article  Google Scholar 

  • Yuan-Chyuan, L., & Chi-Jui, W. (1995). Damping of power system oscillations with output feedback and strip eigenvalue assignment. IEEE Transactions on Power Apparatus and Systems, 10(3), 1620–1626.

    Article  Google Scholar 

  • Zadeh, N. H., & Kalam, A. (1999). A direct adaptive fuzzy power system stabilizer. IEEE Transactions on Energy Conversion, 14(4), 1564–1571.

    Article  Google Scholar 

  • Zhu, C., Khammash, M., Vittal, V., & Qiu, W. (2003). Robust power system stabilizer design using H∞ loop shaping approach. IEEE Transactions on Power Systems, 18(2), 810–818.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. LACoSERE Laboratory, Electrical Engineering Department, Laghouat University, Ghardaïa Street, BP 37, 03000, Laghouat, Algeria

    Brahim Douidi & Lakhdar Mokrani

  2. IREENA, 37 Boulevard de l’Université, BP 406, 44602, Saint-Nazaire, Nantes, France

    Mohamed Machmoum

Authors
  1. Brahim Douidi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lakhdar Mokrani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohamed Machmoum
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lakhdar Mokrani.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

See Tables 6, 7, and 8.

Table 6 Participation factors of the WSCC three generators
Full size table
Table 7 Optimal values of the WSCC CPSS parameters
Full size table
Table 8 Optimal values of the WSCC CFPSS parameters
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Douidi, B., Mokrani, L. & Machmoum, M. A New Cascade Fuzzy Power System Stabilizer for Multi-machine System Stability Enhancement. J Control Autom Electr Syst 30, 765–779 (2019). https://doi.org/10.1007/s40313-019-00486-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40313-019-00486-7

Keywords

Search

Navigation