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Research on hot spot temperature modeling and forecasting method of oil-immersed transformer
编号：92 访问权限：仅限参会人 更新：2024-08-15 10:49:38 浏览：114次 口头报告

报告开始：暂无开始时间（Asia/Shanghai）

报告时间：暂无持续时间

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摘要
    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.
关键词
Oil-immersed transformer,hot-spot temperature prediction,BP neural network,particle swarm algorithm optimization
报告人
Jiali Liu
postgraduate Shandong University

稿件作者
Jiali Liu Shandong University
Hongshun Liu Shandong University
Yizhen Sui Shandong University
Ali Mohammed Ali Abdo Shandong University
Jingtong Feng Shandong University
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重要日期

  • 会议日期

    11月06日

    2024

    至

    11月08日

    2024

  • 09月15日 2024

    初稿截稿日期

  • 11月08日 2024

    注册截止日期

主办单位
Huazhong University of Science and Technology
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