Prediction of Transformer Top Oil Temperature Based on Bayesian Optimization and LSTM Neural Network-Reference-Cited by-同舟云学术

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Prediction of Transformer Top Oil Temperature Based on Bayesian Optimization and LSTM Neural Network

Published:2023-09-21 Issue: Volume: Page:
ISSN:
Container-title:2023 International Conference on Power System Technology (PowerCon)
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Author:

Sui Yizhen¹,Yan Kun²,Abdo Ali Mohammed Ali¹,Zhang Hongbin¹,Liu Hongshun¹,Cong Haoxi³

Affiliation:

1. Shandong University,Shandong Provincial Key Laboratory of UHV Transmission Technology and Equipment,Jinan,China

2. Tangshan Power Supply Company, State Grid Jibei Electric Power Co., Ltd,Tangshan,China

3. North China Electric Power University,State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources,Beijing,China

Funder

State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (NCEPU)

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10330799/10330806/10331427.pdf?arnumber=10331427

Reference15 articles.

1. IEEE Guide for Determination of Maximum Winding Temperature Rise in Liquid-Filled Transformers;IEEE Std 1538–2000,2000

2. IEEE Guide for Loading Mineral-Oil-Immersed Transformers and Step-Voltage Regulators - Redline;IEEE Std C57.91–2011 (Revision of IEEE Std C57.91-1995) - Redline,2012

3. Novel Hotspot Temperature Prediction of Oil-Immersed Distribution Transformers: An Experimental Case Study

4. A Monitoring Method for Average Winding and Hot-Spot Temperatures of Single-Phase, Oil-Immersed Transformers

5. Numerical analysis of the hot-spot temperature of a power transformer with alternative dielectric liquids

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