• DocumentCode
    1984858
  • Title

    Distribution transformer mid-term heavy load and overload pre-warning based on logistic regression

  • Author

    Ming Li ; Qin Zhou

  • Author_Institution
    Technol. Labs., SMIEEE, Beijing, China
  • fYear
    2015
  • fDate
    June 29 2015-July 2 2015
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    in areas with rapid economic growth, distribution transformer heavy load and overload occur frequently, which may damage the equipment and even lead to power outages. Therefore, it is critical for the utilities to know which distribution transformers are more likely to have the heavy load /overload conditions in the next year in order to facilitate asset management in distribution network. However, current load forecasting methods are not suitable for handling the large amount of distribution transformers with a high variety of load patterns. Utilizing real data from a utility, a mid-term pre-warning analytics model has been developed to provide the heavy load and overload probabilities in the next year for each distribution transformer in an area. The mid-term pre-warning models have been implemented in a major utility in China.
  • Keywords
    asset management; distribution networks; load forecasting; power transformers; probability; regression analysis; asset management; distribution network; distribution transformer; heavy load conditions; heavy load pre-warning; load forecasting methods; load patterns; logistic regression; mid-term pre-warning analytics model; overload conditions; overload pre-warning; power outages; Data models; Load modeling; Loading; Logistics; Predictive models; Testing; Training; Distribution Transformer; Logistic Regression; Overload; Pre-warning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    PowerTech, 2015 IEEE Eindhoven
  • Conference_Location
    Eindhoven
  • Type

    conf

  • DOI
    10.1109/PTC.2015.7232418
  • Filename
    7232418