Journal of Electrical Engineering and Technology

  • 제8권5호
  • /
  • Pages.984-990
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  • 2013
  • /
  • 1975-0102(pISSN)
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  • 2093-7423(eISSN)
대한전기학회 (The Korean Institute of Electrical Engineers)
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An Innovative Application Method of Monthly Load Forecasting for Smart IEDs

  • Choi, Myeon-Song (Dept. of Electrical Engineering, Myongji University) ;
  • Xiang, Ling (Dept. of Electrical Engineering, Myongji University) ;
  • Lee, Seung-Jae (Dept. of Electrical Engineering, Myongji University) ;
  • Kim, Tae-Wan (Dept. of Electrical Engineering, Myongji University)
  • 투고 : 2012.04.30
  • 심사 : 2013.04.29
  • 발행 : 2013.09.01
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초록

This paper develops a new Intelligent Electronic Device (IED), and then presents an application method of a monthly load forecasting algorithm on the smart IEDs. A Multiple Linear Regression (MLR) model implemented with Recursive Least Square (RLS) estimation is established in the algorithm. Case Study proves the accuracy and reliability of this algorithm and demonstrates the practical meanings through designed screens. The application method shows the general way to make use of IED's smart characteristics and thereby reveals a broad prospect of smart function realization in application.

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참고문헌

  1. James A. Momoh, Electric Power Distribution, Automation, Protection, And Control: CRC Press, 2007, p. 289.
  2. Joe H. Chow, Felix F. Wu, James A. Momoh, Applied mathematics for restructured electric power systems: optimization, control, and computational intelligence: Springer, 2005, p.269-280.
  3. F. J. Marin, F. Sandoval, "Short-Term Peak Load Forecasting: Statistical Methods versus Artificial Neural Networks", Biological and Artificial Computation: From Neuroscience to Technology Lecture Notes in Computer Science, Volume 1240, pp 1334-1343, 1997.
  4. H. Daneshi, A. Daneshi, "Real Time Load Forecast in Power System", Electric Utility Deregulation and Restructuring and Power Technologies, 2008. DRPT 2008. Third International Conference on, Vol., No., pp. 689, 695, 6-9 April 2008.
  5. Dong C. Park, Osama Mohammed, "Artificial Neural Network Based Electric Peak Load Forecasting", in IEEE Proceedings of SOUTHEASTCON '91 (Cat. No.91CH2998-3),1991.
  6. S. A. Soliman, S. Persaud, K. El-Nagar, and M. E. El-Hawary, "Application of least absolute value parameter estimation technique based on linear programming to short-term load forecasting," in Proceedings of 1996 Canadian Conference on Electrical and Computer Engineering, 1996.
  7. Gershenfeld, The Nature of Mathematical Modeling. Cambridge University Press, 1999, p.116.
  8. L.G. Hewitson, Mark Brown, Ramesh Balakrishnan, Practical power system protection: Elsevier, 2005, p. 127-129.
  9. N. Amral, C.S. Ozveren and D. King, "Short term load forecasting using Multiple Linear Regression," in Proceedings of Universities Power Engineering Conference, 2007.
  10. M.S. Owayedh, A.A. Al-Bassam and Z.R. Khan, "Identification of temperature and social events effects on weekly demand behavior," in Proceedings of IEEE Power Engineering Society Summer Meeting, 2000.
  11. P. Bunnoon, K. Chalermyanont and C. Limsakul, "Mid Term Load Forecasting of the Country Using Statistical Methodology: Case study in Thailand," in Proceedings of 2009 International Conference on Signal Processing Systems, 2009.
  12. R. Ramanathan, R. F. Engle, C. Granger, F.Vahid-Arahi, and C. Brace, "Short-run forecasts of electricity loads and peaks", International Journal of Forecasting, Vol. 13, pp. 161-174, 1997. https://doi.org/10.1016/S0169-2070(97)00015-0
  13. Monson H. Hayes, Statistical Digital Signal Processing and Modeling: Wiley, 1996, p. 541.
  14. John Casazza, Frank Delea, Understanding Electronic Power System: An Overview of the Technology and the Marketplace: Wiley, p.50, 107, 2003.