• DocumentCode
    2896368
  • Title

    Cornish-fisher expansion for probabilistic power flow of the distribution system with wind energy system

  • Author

    Shu-jun, Yao ; Yan, Wang

  • Author_Institution
    North China Electr. Power Univ., Beijing, China
  • fYear
    2011
  • fDate
    6-9 July 2011
  • Firstpage
    1378
  • Lastpage
    1383
  • Abstract
    DG based on wind energy system (WES) has the probabilistic output power relied on natural conditions, the traditional deterministic load flow is not a suitable tool for the analyzing and maintaining distribution system. Aiming at this problem, this paper applies a probabilistic power flow (PLF) method to evaluate the influence of distributed generation with WES. Firstly, the probabilistic model of WES is established which considers the relationship between output power of WES and wind velocity. Secondly, instead of the conventional Gram-Charlier expansion, the model of combined cumulant and Cornish-Fisher expansion is applied to calculate the PLF. This model has the more convergence for on-Gaussian probabilistic density function. Lastly, the IEEE 34 distribution system with the modification by injecting the WES to some nodes is tested. The results show that the method in this paper is efficient and reasonable.
  • Keywords
    distributed power generation; load flow; probability; wind power; Cornish-Fisher expansion; Gram-Charlier expansion; IEEE 34 distribution system; cumulant expansion; distributed generation; on-Gaussian probabilistic density function; probabilistic power flow; wind energy system; wind velocity; Accuracy; Load flow; Load modeling; Probabilistic logic; Wind power generation; Wind speed; Cornish-Fisher; combined cumulants and Gram-Charlier; distributed generation; probabilistic power flow; wind energy system;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electric Utility Deregulation and Restructuring and Power Technologies (DRPT), 2011 4th International Conference on
  • Conference_Location
    Weihai, Shandong
  • Print_ISBN
    978-1-4577-0364-5
  • Type

    conf

  • DOI
    10.1109/DRPT.2011.5994111
  • Filename
    5994111