摘要详情
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
终稿
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.
重要日期
-
会议日期
11月06日
2024
至11月08日
2024
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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
联系方式
历届会议
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2023年12月08日 中国 Chengdu
4th China International Youth Conference on Electrical Engineering -
2023年07月11日 中国 Chengdu
2nd China International Youth Conference on Electrical Engineering -
2022年11月03日 中国 Wuhan
2022 IEEE 3rd China International Youth Conference on Electrical Engineering -
2020年11月02日 中国 Wuhan
China International Youth Conference on Electrical Engineering
