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Voltage Sag and Swell Estimation Using ANFIS for Power System Applications

  • Malmurugan, N. (Mahendra Engineering College, Mallasamudram) ;
  • Gopal, Devarajan (Department of Electrical & Computer Engineering, Mahendra Engineering College) ;
  • Lho, Young Hwan (Department of Railroad Electricity System, Woosong University)
  • Received : 2013.08.07
  • Accepted : 2013.08.27
  • Published : 2013.08.31

Abstract

Power quality is a term that is now extensively used in power systems applications, and in this context the voltage, current, and phase angle are discussed widely. In particular, different algorithms that are capable of detecting the voltage sag and swell information in a real time environment have been proposed and developed. Voltage sag and swell play an important role in determining the stability, quality, and operation of a power system. This paper presents ANFIS (Adaptive Network based Fuzzy Inference System) models with different membership functions to build the voltage shape with the knowledge of known system parameters, and detect voltage sag and swell accurately. The performance of each method has been compared with each other/other methods to determine the effectiveness of the different models, and the results are presented.

Keywords

References

  1. M.H. J. Bollen (1999) Understanding Power Quality Problems: Voltage Sags and Interruptions, IEEE Press, Vol. I, NY.
  2. Roger Dugan, Surya Santoso, Mark McGranaghan, H. Beaty (2004) Electric Power Systems Quality, McGraw-Hill, NY.
  3. P. Wang, N. Jenkins, M. H. J. Bollen (1998) Experimental investigation of voltage sag mitigation by an advanced static VAr compensator IEEE Trans. Power Del., 13(4), pp. 1461-1467. https://doi.org/10.1109/61.714772
  4. X. Xiangning, X. Yonghai, L. Lianguang (2000) Simulation and analysis of voltage sag mitigation using active series volt-age injection, Proc. Int. Conf. Power System Technology, Perth, WA, pp. 1317-1322.
  5. N.S. Tunaboylu, E.R. Collins, Jr., P.R. Chaney (1998) Voltage disturbance evaluation using the missing voltage technique, Proc. 8th Int. Conf. Harmonics and Quality of Power, Athens, pp. 577-582.
  6. M. Goldstein, P.D. Speranza (1994) The Quality of US Commercial AC Power American Power Conference, PA.
  7. Raj Naidoo and Pragasen Pillay (2007) A new method of voltage sag and swell detection IEEE Trans. on Power Del., 22(2), pp. 1056-1062. https://doi.org/10.1109/TPWRD.2007.893185
  8. C. Kocaman, M. Ozdemir (2009) Comparison of statistical methods and wavelet energy coefficients for determining two common PQ disturbances: Sag and Swell International Conference on Electrical and Electronics Engineering, Bursa, Issue 5-8, pp. I-80-I-84.
  9. B. Bae, J. Jeong, J. Lee, B. Han (2010) Novel Sag Detection Method for Line-Interactive Dynamic Voltage Restorer IEEE Trans. on Power Del., 25(2), pp. 1210-1211. https://doi.org/10.1109/TPWRD.2009.2037520
  10. Jyh-shing (1993) ANFIS: Adaptive-Network Based Fuzzy Inference System. IEEE Trans. on Systems, Man, and Cybernetics, 23(3), pp. 665-685. https://doi.org/10.1109/21.256541