Forecasting Method of Branch Power of the Urban Power Grid Based on Multi-Model Technology

Abstract:

An adaptive forecasting method of branch power of the urban power grid is presented. The implementation is accomplished by utilizing three basic load forecasting methods using Multi-model technology. After forecasting the load of one bus, two methods are used to get the branch power including distributing bus load and forecasting it directly with history data. The proposed branch power forecasting method is tested on one urban power grid. The simulations imply that the adaptive method is more efficient than that using single model.

• Periodical: Advanced Materials Research (Volumes 354-355)
• Pages: 1064-1067
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.354-355.1064
• Online since: October 2011
• Authors: Xiao Jing Wang, Xing Ying Chen, Kun Yu
• Keywords: Branch Power Forecasting, Multi-Model Technology, Super Short-Time Load Forcasting

References

[1] J.Hughes, IntelliGrid architecture concepts and IEC 61850, Proceedings of IEEE PES Transmission and Distribution Conference and Exhibition. May 21-24, 2006, Dallas, TX, USA.

[2] S. Rahman, M. Pipattanasomporn, and Y.Teklu, Intelligent distributed autonomous power systems (IDAPS), Proceedings of IEEE Power Engineering Society General Meeting. June 24-26, 2007, Tampa, FL, USA.

DOI: 10.1109/pes.2007.386043

[3] M.Amin, Toward a self-healing energy infrastructure, Proceedings of IEEE Power Engineering Society General Meeting. June 18-22, 2006, Montreal, Canada.

DOI: 10.1109/pes.2006.1709607

[4] K. Moslehi, A. B. R. Kumar, D. shurtleff, et al, Framework for a self-healing power grid, Proceedings of IEEE Power Engineering Society General Meeting. June 12-16, 2005, Montreal, Canada.

DOI: 10.1109/pes.2005.1489709

[5] Nima Amjady, Farshid Keynia, and Hamidreza Zareipour, Short-term load forecast of microgrids by a new Bilevel prediction strategy, IEEE Transactions on Power Systems, December 2010, 1(3) : 286-294.

DOI: 10.1109/tsg.2010.2078842

[6] V.H. Hinojosa, A.Hoese, Short-term load forecasting using fuzzy inductive reasoning and evolutionary algorithms, IEEE Transactions on power systems, February 2010, 25(1) 565-574.

DOI: 10.1109/tpwrs.2009.2036821

[7] H.M.Al-Hamadi, S.A. Soliman, Fuzzy short-term electric load forecasting using Kalman filter, IEE Generation, Transmission and Distribution, Vol.153, No.2, March 2006, 217-227.

DOI: 10.1049/ip-gtd:20050088

[8] M.A. Abu-El-Magd, and R.D. Findlay, A new approach using artificial neural network and time series models for short term load forecasting, CCGEI 2003. May 2003, Montreal, Canada, 1723-1726.

DOI: 10.1109/ccece.2003.1226242

[9] T.Senjyu, S.Higa, and K.Uezato, Future load curve shaping based on similarity using fuzzy logic approach, IEE Generation, Transmission and Distribution. Vol.145, No.4, July 1998, 375-380.

DOI: 10.1049/ip-gtd:19981998

[10] Nasser Sadati, Guy A.Dumont, and H.R. Feyz Mahdvian, Robust multiple model adaptive control using fuzzy fusion, 42nd South Eastern Symposium on System Theory University of Taxas Tyler. Tyler, TX, March 7-9, (2010)

DOI: 10.1109/ssst.2010.5442796