• DocumentCode
    2020020
  • Title

    Benchmark low voltage distribution networks based on cluster analysis of actual grid properties

  • Author

    Dickert, Joerg ; Domagk, M. ; Schegner, Peter

  • Author_Institution
    Inst. of Electr. Power Syst. & High Voltage Eng., Tech. Univ. Dresden, Dresden, Germany
  • fYear
    2013
  • fDate
    16-20 June 2013
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    Distribution systems are supplying many customers with electricity requiring a high number of equipment and lines. It is tedious to analyze all low-voltage (LV) networks of a distribution system operator (DSO). Because of the large number of LV-networks within the system benchmark networks are required. For bulk analysis the networks have to represent a large number of networks within the system. For extreme value analysis the most critical networks have to be identified and examined. Benchmark networks representing typical LV-networks for bulk analysis can be derived from technical data of these networks using multivariate methods. This paper describes the data allocation and application of cluster analysis for this purpose. The presented benchmark networks are based on German conditions. The networks are also reviewed under the aspect of feasibility. The benchmark networks are characterized by their supply obligation as well as the network data and properties are given for benchmark analysis. The paper also includes the diverse experience in data allocation, analysis of the data and gives assistants for prospective data allocation combined with cluster analysis of LV-networks. With the information given in the paper also benchmark networks adapted to a variety of other applications can be derived.
  • Keywords
    distribution networks; statistical analysis; actual grid properties; benchmark low voltage distribution networks; cluster analysis; distribution system operator; low-voltage networks; Benchmark testing; Data analysis; Impedance; Power cables; Principal component analysis; Resource management; clustering methods; data handling; multivariate methods; power system planning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    PowerTech (POWERTECH), 2013 IEEE Grenoble
  • Conference_Location
    Grenoble
  • Type

    conf

  • DOI
    10.1109/PTC.2013.6652250
  • Filename
    6652250