• DocumentCode
    3470237
  • Title

    An optimized FCM method for electric load clustering

  • Author

    Li, Cailing ; Wang, Jin ; Li, Xinran

  • Author_Institution
    Coll. of Electr. & Inf. Eng., Univ. of Sci. & Technol., Changsha
  • fYear
    2008
  • fDate
    6-9 April 2008
  • Firstpage
    882
  • Lastpage
    886
  • Abstract
    Load modeling is known as one of the most difficult problems in the power system of the world. It is necessary to further research on the load characteristics analysis and load model can be constructed practically via appropriate clustering method to increase the precision and credibility of the power system analysis, control and simulation. Considering the deficiency of traditional hard C-means (HCM), this paper presents an optimized fuzzy C-means (FCM) method for the static load characteristics clustering of 48 substations in Hunan province power grid. Numbers of substation nodes to install measurement units and the location of substations can be inferred from the clustering results, which provides a significant approach for the research on practical load modeling engineering.
  • Keywords
    fuzzy set theory; load (electric); optimisation; power grids; power systems; substations; Hunan province power grid; electric load clustering; fuzzy set theory; hard C-means method; load modeling; optimization theory; optimized FCM method; optimized fuzzy C-means method; power system analysis; power system control; power system simulation; static load characteristics; substations; Clustering methods; Load modeling; Optimization methods; Power system analysis computing; Power system control; Power system measurements; Power system modeling; Power system simulation; Power systems; Substations; clustering analysis; fuzzy sets; load characteristics; load modeling; optimized FCM method;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electric Utility Deregulation and Restructuring and Power Technologies, 2008. DRPT 2008. Third International Conference on
  • Conference_Location
    Nanjuing
  • Print_ISBN
    978-7-900714-13-8
  • Electronic_ISBN
    978-7-900714-13-8
  • Type

    conf

  • DOI
    10.1109/DRPT.2008.4523531
  • Filename
    4523531