Advertisement

Design of Intelligent Controllers Using Online-Trained Fuzzy Neural Networks for the UPFC

  • Tsao-Tsung Ma
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 4)

With the trend toward deregulating the power industry, it has been expected that future power systems will need to provide sufficient, secure, high-quality and real-time controllable electric power to various load centers. It is envisaged that flexible AC transmission systems (FACTS) devices [1,2] or similar power flow controllers are going to play a critical role in operating the new type of power systems under such a complex operating environment. Basically, using state-of-the-art communication technologies, high-power converters and properly designed controllers, FACTS devices can offer great control flexibility in power system operations. It has been proved that a number of important control functions and optimization issues, which were unattainable in the past, can now be easily realized by utilizing these high-performance power flow controllers with some properly developed control algorithms. One of the crucial objectives concerning FACTS device applications in modern power systems is real-time load flow control. Of the reported FACTS devices, it has been well accepted that the unified power flow controller (UPFC) is the most versatile and powerful one [3, 4] that can provide effective means for controlling the power flow and improving the transient stability of a power network [5, 6]. However, the UPFC has multiple operating modes and complex system dynamics that require advanced controllers to achieve satisfactory performances. In the literature, a number of conventional controllers have been proposed for the UPFC to perform various power system control functions; however, most of them are linear controllers designed on a linearized model of the controlled power system. To achieve robust control effects and better dynamic control performance some advanced controllers with adaptive features and the ability to learn will be required.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Hingorani NG (1993) Flexible AC transmission. IEEE Spectrum 1:40–45CrossRefGoogle Scholar
  2. 2.
    Edris A-A (1997) Proposed terms and definitions for flexible AC transmission systems (FACTS). IEEE Trans. on Power Delivery 12(4): 1848–1853CrossRefGoogle Scholar
  3. 3.
    Gyugyi L (1992) Unified power-flow control concept for flexible AC transmission system. IEEE Proceedings-C, 139 (4):323–33lGoogle Scholar
  4. 4.
    Schauder CD, Hamai DM, Edris A (1998) Operation of the unified power flow controller (UPFC) under practical constraints. IEEE Trans on Power Delivery 13(2): 630–639CrossRefGoogle Scholar
  5. 5.
    Lo KL, Ma TT (2001) UPFC damping control strategy based on transient energy function. The International Journal of Electric Power Systems Research (EPSR) 56:195–203CrossRefGoogle Scholar
  6. 6.
    Lo KL, Ma TT (2000) A robust UPFC damping scheme using pi and ann based adaptive controllers. The International Journal for Computation and Mathematics in Electrical Engineering (COMPEL) (19)3:878–902zbMATHCrossRefGoogle Scholar
  7. 7.
    Wu B, Malik OP (2006) Multivariable adaptive control of synchronous machines in a multimachine power system. IEEE Trans. on Power Systems. 21(4):1772–1781CrossRefGoogle Scholar
  8. 8.
    Amjady N (2006) Generation adequacy assessment of power systems by time series and fuzzy neural network. IEEE Trans. on Power Systems. 21(3):1340–1349CrossRefGoogle Scholar
  9. 9.
    Narendra KS, Parthasarathy K (1990) Identification and control of dynamical system using neural networks. IEEE Trans. Neural Networks 1:4–27CrossRefGoogle Scholar
  10. 10.
    Venayagamoorthy GK, Harley RG (2001) A continually online trained neurocontroller for excitation and turbine control of a turbo generator. IEEE Trans. on Energy Conversion 16(3):261–269CrossRefGoogle Scholar
  11. 11.
    Heinzinger G, Fenwick D, Paden B, Miyazaki F (2001) Stability of Takagi-Sugeno recurrent fuzzy neural networks for identification and control of dynamic systems. In: Proc. IEEE Int. Conf. on Fuzzy Systems, Melbourne, Australia, pp 537–540Google Scholar
  12. 12.
    Lo KL, Ma TT, Trecat J, Crappe M (1998) Detailed real-time simulation and performance analysis of UPFC using electromagnetic transients program (EMTP). In: Proc. of POWERCON ’98, Beijing, China, 1, pp 889–894Google Scholar
  13. 13.
    Dong LY, Zhang L, Crow ML (2002) A new control strategy for the unified power flow controller. In: Proc. of IEEE-PES Winter Meeting, 1, pp 562–566Google Scholar
  14. 14.
    Leu YG, Lee TT, Wang WY (1997) On-line tuning of fuzzy-neural network for adaptive control of nonlinear dynamical systems. IEEE Trans. Syst. Man Cybern. 1: pp 1034–1043Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Tsao-Tsung Ma
    • 1
  1. 1.Department of EE, CEECSNational United UniversityChina

Personalised recommendations