Abstract Details
  1. Home
  2. Abstract List
  3. Abstract Details
116 / 2024-07-31 17:40:32
Research on hot spot temperature modeling and forecasting method of oil-immersed transformer
Oil-immersed transformer,hot-spot temperature prediction,BP neural network,particle swarm algorithm optimization
4.High Voltage and Insulation Technology > 1.High voltage testing and measurement
Final Paper
Hongshun Liu / Shandong University
Ali Mohammed Ali Abdo / Shandong University
Yizhen Sui / Shandong University
Jingtong Feng / Shandong University
Jiali Liu / Shandong University
    The hot-spot temperature of transformer is the highest point of winding temperature during transformer operation, which directly reflects the heat load of winding and is an important index for transformer safety and performance evaluation. Therefore, accurate prediction of transformer hot spot temperature is of great significance for estimating transformer service life, improving economic benefits and preventing major thermal accidents. Based on the in-depth analysis of the internal temperature rise process and temperature distribution characteristics of the oil-immersed transformer, and combined with the measured data of the transformer after removing outliers, a transformer hot spot temperature prediction model based on BP neural network is built to achieve a high precision prediction of the hot spot temperature of the oil-immersed transformer winding. Use particle group algorithm to optimize BP neural network and to further improve the accuracy and reliability of the model prediction.
Important Date

  • Conference Date

    Nov 06

    2024

    to

    Nov 08

    2024

  • Sep 15 2024

    Draft paper submission deadline

  • Nov 08 2024

    Registration deadline

Sponsored By
Huazhong University of Science and Technology
Contact Information
Login to view full contact details
Previous Conferences