Application of ANN to Load Modeling in Power System Analysis

  • Jaeyoon Lim (Dept. Electrical Engineering, Daeduk College) ;
  • Lee, Jongpil (Dept. Electrical Engineering, Daeduk College) ;
  • Pyeongshik Ji (Dept. Electrical Engineering, Chungju National University) ;
  • A. Ozdemir (Dept. Electrical Engineering, Istanbul Technical University) ;
  • C. Singh (Dept. Electrical Engineering, Texas A&M University, U.S.A.)
  • 발행 : 2002.04.01

초록

Load models are very important for improving the accuracy of stability analysis and load flow studies. Various loads are connected to a power bus and their characteristics of power consumption change with voltage and frequency. Thus, the effect of voltage/frequency changes must be considered in load modeling. In this work, artificial neural networks-ANNs- were used to construct the component load models for more accurate modeling. A typical residential load was selected and subjected to a test under variable voltage/frequency conditions. Acquired data were used to construct component models by ANNs. The aggregation process of separately determined load models is also presented in the paper. Furthermore, this paper proposes a method to transform a single load model constructed by the aggregation method into a mathematical load model that can be used in traditional power system analysis software.

키워드

참고문헌

  1. General Electric Co., 'Load Models for Power Flow and Transient Stability Computer Studies,' EL-5003, Vol. 1-4, EPRI Project 847-7,1987
  2. N. Vempati, R. R. Shoults, M. S. Chen, and L. Schwobel, 'Simplified Feeder Modeling for Load Flow Calculations,' IEEE Trans. on PWRS, Vol. PWRS-2, No.1, pp. 168-174,1987
  3. IEEE Task Force on Load Representation for Dynamic Performance, 'Standard load models for power flow and dynamic performance simulation,' IEEE Trans. on Power Systems, Vol. 10, No.3, Aug. 1995, pp. 1302-1313 https://doi.org/10.1109/59.466523
  4. UTA, G.E., and IREQ, Determining Load Character-istics for Transient Performances, EPRI Report EL 849 Vol. 1-3,1979
  5. P. Kundur, Power System Stability and Control, McGraw-Hill, 1993
  6. J. Y. Lim, J. H. Kim, 1. O. Kim, and C. Singh 'Ap-plication of Expert System to Load Composition Rate Estimation Algorithm,' IEEE Trans. on Power Sys-tems, Vol. 14, No.3, August 1999, pp. 1137-1143
  7. M. Bostanci, J. Koplowitz, and C.W. Taylor, 'Identi-fication of Power System Load Dynamics Using Artificial Neural Networks,' IEEE Transactions on Power Systems, Vol. 12, No.4, November 1997, pp. 1468-1473
  8. T. Hiyama, M. Tokieda, W. Hubbi, and H. Andou, 'Artificial Neural Network Based Dynamic Load Modeling,' IEEE Transactions on Power Systems, Vol. 12, No.4, November 1997, pp. 1576-1583 https://doi.org/10.1109/59.627861
  9. J. P. Lee, P. S. Ji, T. E. Kim, S. C. Nam, J. H. Kim, and J. Y. Lim, 'Load Characteristic Identification Using Artificial Neural Network,' 1997 International Con-ference on Electrical Engineering, pp. 592-595
  10. T. E. Kim, P. S. Ji, J. P. Lee, S. C. Nam, J. H. Kim, and J. Y. Lim, 'Load Characteristic Identification Using Artificial Neural Network and Transient Stability Analysis,' 1998 International Conference on Energy Management and Power Delivery, Vol. 1, pp. 329-334
  11. J. M. Zurada, Introduction to Artificial Neural Systems, West, 1992
  12. P. W. Sauer, and M. A. Pai, Power System Dynamics and Stability, Prentice Hall, Inc. 1998
  13. K. R. Padiyar, Power System Dynamics: Stability and control, John Wiley & Sons, 1996