• DocumentCode
    478256
  • Title

    A Novel Method for Power Quality Comprehensive Evaluation Based on ANN and Subordinate Degree

  • Author

    Yuan, Shuai ; Tong, Weiming ; Tong, Chengde ; Li, Zhongwei

  • Author_Institution
    Dept. of Electr. Eng. & Autom., Harbin Inst. of Technol., Harbin
  • Volume
    4
  • fYear
    2008
  • fDate
    18-20 Oct. 2008
  • Firstpage
    62
  • Lastpage
    65
  • Abstract
    A comprehensive evaluation approach of power quality (PQ) based on subordinate degree-BP neural network was proposed in this paper. In case the BP neural network training process is trapped by the local minimum point, genetic algorithm (GA) was introduced to optimize the network´s initial weights. A large number of samples based on the random-distribution theory were produced to train the network, and the network output results were analyzed according to the subordinate degree rule. Compared with the BP network neural method, the proposed subordinate degree-BP neural network method can evaluate the PQ level correctly and analyze all kinds of PQ indices exactly. By practically evaluating the 0.38 kV distribution network, the proposed approach is proved correct and feasible.
  • Keywords
    backpropagation; distribution networks; genetic algorithms; minimisation; neural nets; power engineering computing; power supply quality; random processes; statistical distributions; ANN; genetic algorithm; local minimum point; power 0.38 kW; power distribution network; power quality comprehensive evaluation; random-distribution theory; subordinate degree BP neural network training; Artificial neural networks; Biological cells; Computer networks; Frequency; Genetic algorithms; Neural networks; Power engineering computing; Power quality; Power supplies; Voltage; genetic algorithm; neural network; power quality comprehensive evaluation; subordinate degree;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Natural Computation, 2008. ICNC '08. Fourth International Conference on
  • Conference_Location
    Jinan
  • Print_ISBN
    978-0-7695-3304-9
  • Type

    conf

  • DOI
    10.1109/ICNC.2008.543
  • Filename
    4667249