• DocumentCode
    3103032
  • Title

    Intelligent Approach for Distribution System Load Estimation

  • Author

    Ramesh, L. ; Chowdhury, S. ; Chowdhury, S.P. ; Natarajan, A.A. ; Taylor, G.A. ; Song, Y.H.

  • Author_Institution
    E.E.Dept., Dr.MGR Univ., Chennai
  • fYear
    2007
  • fDate
    24-28 June 2007
  • Firstpage
    1
  • Lastpage
    7
  • Abstract
    The operation of distribution systems requires a rather high number of planned or forced switching operations. In order to prepare them, it is necessary to estimate loads at different levels in the system. This paper proposes a load estimation method for distribution systems with weighted least square estimation and back propagation artificial neural network. The above methods are demonstrated with IEEE 14 bus distribution system with comparison of simulated estimated outputs. The purpose of the load estimation method presented in this paper is the economic and efficient use of the available remote measurements in the distribution system considering typical measurements from the past and knowledge of load composition and load behavior at the distribution transformers´ level.
  • Keywords
    backpropagation; distribution networks; least mean squares methods; neural nets; power engineering computing; power system state estimation; back propagation artificial neural network; distribution system load estimation; distribution transformer; state estimation; weighted least square estimation; Automatic control; Automatic meter reading; Least squares approximation; Load management; Meter reading; Power distribution; Power generation economics; Power system planning; Production planning; Switches; Distribution systems; State estimation; load estimation; neural network; weighted least squares;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power Engineering Society General Meeting, 2007. IEEE
  • Conference_Location
    Tampa, FL
  • ISSN
    1932-5517
  • Print_ISBN
    1-4244-1296-X
  • Electronic_ISBN
    1932-5517
  • Type

    conf

  • DOI
    10.1109/PES.2007.386154
  • Filename
    4275920