• DocumentCode
    1065522
  • Title

    A systematic loss analysis of Taipower distribution system

  • Author

    Kang, Meei-Song ; Chen, Chao-Shun ; Lin, Chia-Hung ; Huang, Chia-Wen ; Kao, Meng-Fu

  • Author_Institution
    Dept. of Electr. Eng., Univ. of Kao Yuan, Kaohsiung
  • Volume
    21
  • Issue
    3
  • fYear
    2006
  • Firstpage
    1062
  • Lastpage
    1068
  • Abstract
    This paper presents a systematic methodology to analyze the power loss of whole distribution system in Taipower. The total power delivered to the distribution system has been calculated according to the total power generation and power loss of transmission system. To enhance the efficiency for power loss analysis of voluminous distribution feeders, the artificial neural network (ANN)-based simplified power loss models have been developed for the overhead feeders and underground feeders, respectively. The three-phase load flow analysis is executed to find the sensitivity of feeder loss with the variation of power loading, conductor length, and total capacity of distribution transformers. By this way, the data set for neural network training is prepared to derive the ANN-based simplified power loss model. The power loss of each distribution feeder can be derived easily according to the key factors of hourly loading, feeder length, and transformer capacity. By integrating the power loss of all feeders, the power loss of whole distribution system is therefore obtained to estimate the operation efficiency of Taipower system
  • Keywords
    load flow; losses; neural nets; power distribution lines; power engineering computing; power transformers; ANN; Taipower distribution system; artificial neural network; distribution transformers; feeder loss sensitivity; operation efficiency; overhead feeders; power loading; power loss analysis; systematic loss analysis; three-phase load flow analysis; total power generation; transmission system; underground feeders; voluminous distribution feeders; Artificial neural networks; Chaos; Neural networks; Power demand; Power generation; Power system modeling; Propagation losses; Substations; Transformers; Voltage; Artificial neural network (ANN) power loss model; automated mapping and facility management (AM/FM) system; distribution feeder loss analysis; load profile synthesis;
  • fLanguage
    English
  • Journal_Title
    Power Systems, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0885-8950
  • Type

    jour

  • DOI
    10.1109/TPWRS.2006.879307
  • Filename
    1664939