access icon free Adaptive fuzzy critic based control design for AGC of power system connected via AC/DC tie-lines

This study presents the design of adaptive fuzzy critic based emotional learning control design for automatic generation control (AGC) of a two-area power system interconnected via parallel AC/DC tie-lines. The adaptive fuzzy critic evaluates the current system situation and provides the emotional signal so that the artificial neuro fuzzy regulator to modify its characteristic and reduce the critic stress. The adaptive fuzzy critic based emotional learning control design are implemented and the system dynamic responses are obtained considering 1% load disturbance in area-1. A comparative study of performance of proposed control, fuzzy logic and conventional integral based control is carried out and presented with and without considering the system non-linearities such as governor dead-band and generation rate constraint. The proposed control design technique has been demonstrated as a superior one as compared with other techniques used for the AGC design in the study. Furthermore, the sensitivity analysis of the proposed control is also examined by varying the system parameters over the wide range from the nominal system values.

Inspec keywords: power system control; fuzzy control; sensitivity analysis; automatic gain control; dynamic response; learning (artificial intelligence)

Other keywords: AGC; two-area power system; emotional learning control design; sensitivity analysis; AC/DC tie-line; dynamic response; adaptive fuzzy critic based control design; generation rate constraint; load disturbance; artificial neurofuzzy regulator; automatic generation control; integral based control

Subjects: Power system control; Fuzzy control; Control of electric power systems

References

    1. 1)
      • 10. Sharma, G., Niazi, K.R., Ibraheem, : ‘Recurrent ANN based AGC of a two-area power system with DFIG based wind turbines considering asynchronous tie-lines’. IEEE Int. Conf. on Advances in Engineering and Technology Research (ICAETR-2014), 2014, pp. 15.
    2. 2)
    3. 3)
      • 27. Sutton, R.S.: ‘Temporal credit assignment in reinforcement learning’. Ph.D. Dissertation, University Massachusetts, Amherst, MA, 1984.
    4. 4)
      • 8. Bevrani, H., Hiyama, T.: ‘Intelligent automatic generation control’ (CRC Press, 2011).
    5. 5)
    6. 6)
      • 30. Sharma, G., Niazi, K.R., Ibraheem, : ‘LS-SVM based AGC of an asynchronous power system with dynamic participation from DFIG based wind turbines’, Res. J. Appl. Sci. Eng. Technol., 2014, 8, (8), pp. 10221028.
    7. 7)
    8. 8)
    9. 9)
    10. 10)
    11. 11)
    12. 12)
    13. 13)
    14. 14)
    15. 15)
      • 5. Ibraheem, , Niazi, K.R., Sharma, G.: ‘Study on dynamic participation of wind turbines in AGC of power system’, Electr. Power Compon. Syst., 2014, 43, (1), pp. 4455.
    16. 16)
      • 2. Bevrani, H.: ‘Robust power system control’ (Springer, New York, 2009).
    17. 17)
    18. 18)
      • 23. Sutton, R.S., Barto, A.G.: ‘Reinforcement learning, an introduction’ (MIT Press, 1998).
    19. 19)
    20. 20)
    21. 21)
      • 4. Bansal, R.C.: ‘Automatic reactive power control of autonomous hybrid power systems’. Ph. D Thesis, Indian Institute of Technology (IIT) Delhi, India, April 2003.
    22. 22)
    23. 23)
      • 9. Bansal, R.C.: ‘Overview and literature survey of artificial neural networks applications to power systems (1992–2004)’, J. Inst. Eng. (India) – Electr. Eng., 2006, 86, (1), pp. 282296.
    24. 24)
    25. 25)
      • 3. Bansal, R.C., Bhatti, T.S.: ‘Small signal analysis of isolated hybrid power systems: reactive power and frequency control analysis’ (Alpha Science International, Oxford, UK, 2008).
    26. 26)
    27. 27)
    28. 28)
      • 24. Busoniu, L., Babuska, R., Schutter, B.D., et al: ‘Reinforcement learning and dynamic programming using function approximators’ (CRC Press, 2010).
    29. 29)
    30. 30)
      • 28. Khorramabadi, S.S.: ‘Adaptive critic based control of voltage source converters in microgrid systems’. Ph.D. Dissertation, Queen's University, Ontario, Canada, 2014.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2016.1164
Loading

Related content

content/journals/10.1049/iet-gtd.2016.1164
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading